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WO2006002466A1 - Appareil et procede de traitement d'images - Google Patents

Appareil et procede de traitement d'images Download PDF

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Publication number
WO2006002466A1
WO2006002466A1 PCT/AU2005/000955 AU2005000955W WO2006002466A1 WO 2006002466 A1 WO2006002466 A1 WO 2006002466A1 AU 2005000955 W AU2005000955 W AU 2005000955W WO 2006002466 A1 WO2006002466 A1 WO 2006002466A1
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WO
WIPO (PCT)
Prior art keywords
image
detection
determining
updated
detecting
Prior art date
Application number
PCT/AU2005/000955
Other languages
English (en)
Inventor
John Matthew Naylor
Matthew Paul Fettke
Neil Cameron Thatcher
Andrew Lennox Davis
Original Assignee
Vfs Technologies Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2004903572A external-priority patent/AU2004903572A0/en
Application filed by Vfs Technologies Limited filed Critical Vfs Technologies Limited
Priority to US11/571,476 priority Critical patent/US8295541B2/en
Publication of WO2006002466A1 publication Critical patent/WO2006002466A1/fr

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/02—Mechanical actuation
    • G08B13/14—Mechanical actuation by lifting or attempted removal of hand-portable articles
    • G08B13/1436—Mechanical actuation by lifting or attempted removal of hand-portable articles with motion detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/19—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
    • G08B13/19604—Image analysis to detect motion of the intruder, e.g. by frame subtraction involving reference image or background adaptation with time to compensate for changing conditions, e.g. reference image update on detection of light level change
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19697—Arrangements wherein non-video detectors generate an alarm themselves
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
    • G08B29/02—Monitoring continuously signalling or alarm systems
    • G08B29/04—Monitoring of the detection circuits
    • G08B29/046—Monitoring of the detection circuits prevention of tampering with detection circuits

Definitions

  • the present invention relates to image processing.
  • the invention relates to a method of determining whether an object, situated in a region of interest and viewed in a sequence of images is located in an expected position or has moved, been tampered with or otherwise altered.
  • the present invention relates to a detection system, which in one example relates to a security system capable of monitoring whether a detector forming part of the security system has undergone tampering. It will be convenient to hereinafter describe this embodiment of the invention in relation to the use of a passive infra-red (PIR) detector in a security system.
  • PIR passive infra-red
  • Video camera systems have long been used to monitor areas or regions of interest for the purposes of maintaining security and the like.
  • One important application is the use of video camera systems to monitor sensitive areas in locations such as museums or art galleries which include valuable items that could be potentially removed by a member of the public.
  • a security attendant In this human based scenario, the attendant would be relied on to detect any changes in the areas being viewed by each of the individual cameras.
  • this approach has a number of significant disadvantages. Notwithstanding the expense of the labour involved, this approach is prone to human error as it relies on the ability of the attendant to detect that a change of significance has occurred within the area being viewed by the camera without being distracted by any other visual diversion.
  • a na ⁇ ve approach to this problem includes the direct comparison of either individual or groups of pixel intensities of subsequent sequential images or frames which make up a digital video signal. If the difference between a group of pixels over a number of sequential images is found to be over some threshold then an alarm is generated indicating that movement has occurred within the area being viewed by the camera.
  • this na ⁇ ve approach when applied to a viewing area which naturally includes a subset of objects moving within it (e.g. patrons at a museum) and a number of stationary items (e.g. museum exhibits) fails as the movement of patrons will trigger the alarm.
  • various detection or monitoring systems which may be arranged to provide security or detect and measure the behaviour of objects within a field of view or detection region of the system are well-known. Examples range from Doppler radar detectors used to measure or detect a characteristic such as the speed of vehicles and active beam detectors which measure or detect a characteristic such as the reflection of an incident beam off an object to devices such as passive infra-red (PIR) detectors which measure the characteristic of heat emanated by objects and are often used in security applications.
  • PIR passive infra-red
  • the performance of these devices may be degraded or totally compromised if the actual field of view or detection region is different from that assumed during initial setup.
  • the characteristic of speed calculated by the device will depend on the angle of travel of the moving vehicle with respect to the orientation of the detector and errors in setup may result in erroneous results.
  • this device may typically be located and adjusted to view regions which are required to be kept secure such as an entranceway to a building or the like. If in fact the PIR detector is not pointing in the correct direction, a person moving along the viewed entranceway may not be detected, as they may not be within the field of view of the detector.
  • the present invention accordingly provides a method of detecting change of an object state from an initial state, said object displayed in a plurality of sequential images, said method comprising: comparing a measure over a predetermined portion of each of said images corresponding to an object's initial state with a reference value of said measure computed when said object is in said initial state to generate a comparison value for each of said images; and generating a signal indicating that said object state has changed when a predetermined number of said comparison values generated for each of said images do not meet a predetermined criterion.
  • said measure is substantially illumination invariant.
  • said substantially illumination invariant measure is derived from edge characteristics of said object.
  • plurality of sequential images forms a digital video signal.
  • the present invention accordingly provides a method of detection comprising the steps of: determining a reference image of an object scene comprising a recording of at least one object image feature; determining an updated/ actual/ current image of the object scene comprising a recording of at least one object image feature; comparing the reference and updated/ actual/ current images in accordance with a predetermined comparison metric; invoking an alarm condition when a result of the step of comparing meets one or more of a set of predefined criteria.
  • the set of predefined criteria comprises: a) the predetermined comparison metric indicates a threshold proportion of the updated/ actual/ current image does not match the corresponding proportion of the reference image ; b) a portion of the updated/ actual/ current image does not match the corresponding portion of the reference image during a continuous time interval .
  • the present invention accordingly provides a method of detection comprising the steps of: determining a reference image of an object scene comprising a recording of at least one object image feature; determining an updated/ actual/ current image of the object scene comprising a recording of at least one object image feature; comparing the updated/ actual/ current image to the reference image in accordance with a predetermined comparison metric; invoking a first alarm condition when the predetermined comparison metric indicates a threshold proportion of the updated/ actual/ current image does not match the corresponding proportion of the reference image .
  • the present invention accordingly provides a method of detection comprising the steps of: determining a reference image of an object scene comprising a recording of at least one object image feature; determining an updated/ actual/ current image of the object scene comprising a recording of at least one object image feature; comparing the updated/ actual/ current image to the reference image in accordance with a predetermined comparison metric; invoking a second alarm condition when a portion of the updated/ actual/ current image does not match the corresponding portion of the reference image during a continuous time interval .
  • the present invention accordingly provides a method of detection comprising the steps of: determining a reference image of an object scene comprising a recording of at least one object edge; determining an updated/ actual/ current image of the object scene comprising a recording of at least one object edge; comparing the updated/ actual/ current image edges to the reference image edges in accordance with a predetermined comparison metric; invoking a first alarm condition when one or more portions of the updated/ actual/ current image does not match the corresponding one or more portions of the reference image during a continuous time interval .
  • the method further comprises the steps of: determining the total portion of the updated/ actual/ current image which contributes to invoking the first alarm condition; and invoking a second alarm condition when the determined portion of the updated/ actual/ current image exceeds a threshold proportion of the updated/ actual/ current image .
  • the present invention accordingly provides a computer program product comprising: a computer usable medium having computer readable program code and computer readable system code embodied on said medium for conducting a detection analysis within a data processing system, said computer program product comprising: computer readable code within said computer usable medium for performing the method steps of any one of aspects one to five of the invention.
  • the present invention accordingly provides a device for detecting a characteristic of a detection region, said detection region associated with a detecting direction of said device, said device comprising: detection means to detect said characteristic; and tamper monitoring means to monitor said detecting direction of said device.
  • said tamper monitoring means generates a signal on a change of detecting direction of said device.
  • said tamper monitoring means monitors said change in said detecting direction by image processing means.
  • said image processing means comprises imaging means to view a viewing region related to said detecting direction, said image processing means operable to detect changes in said viewing region corresponding to a change in said detecting direction of said device.
  • said imaging means also comprises said detection means.
  • the present invention accordingly provides a method for monitoring for the alteration or tampering of a detection device, said detection device operable to detect a characteristic of a detection region, said method comprising the steps: viewing a viewing region related to a detecting direction of said detection device; and determining a change in said viewing region associated with a change in said detecting direction.
  • said determining step comprises: detecting a change of an object state from an initial state, said object located in said viewing region and displayed in a plurality of sequential images associated with said viewing region, said detecting step further comprising: comparing a measure over a predetermined portion of each of said images corresponding to an object's initial state with a reference value of said measure computed when said object is in said initial state to generate a comparison value for each of said images; and generating a signal indicating that said object state has changed when a predetermined number of said comparison values generated for each of said images do not meet a predetermined criterion.
  • said detection device further comprises imaging means to perform said viewing of said viewing region and generate said plurality of sequential images.
  • said detection device is dependent on said detecting direction.
  • the present invention accordingly provides a method for determining a contrast measure for an image; said method comprising the steps of determining a plurality of intensity measures associated with a plurality of regions of said image; calculating a frequency value for each of a plurality of intensity ranges in respect of said plurality of intensity measures; and determining said contrast measure based on said frequency values.
  • said step of determining a contrast measure comprises determining a first frequency value corresponding to a maximum intensity range and calculating the difference between this value and a second frequency value corresponding to a minimum intensity range.
  • said first and second frequency values are above a predetermined threshold.
  • the present invention accordingly provides a method for compensating for contrast changes in an image change detection method, wherein said image change detection method is based upon a comparison of a current image with a reference image, said method comprising the steps of: determining a contrast measure for said current image; determining an updated reference image based upon said contrast measure; and comparing said current image with said updated reference image.
  • an apparatus adapted to monitor for the alteration or tampering of a detection device; said apparatus comprising: processor means adapted to operate in accordance with a predetermined instruction set, said apparatus, in conjunction with said instruction set, being adapted to perform the method steps of aspect nine of the invention.
  • an apparatus adapted to determine a contrast measure for an image; said apparatus comprising: processor means adapted to operate in accordance with a predetermined instruction set, said apparatus, in conjunction with said instruction set, being adapted to perform the method steps of aspect ten of the invention.
  • an apparatus adapted to compensate for contrast changes in an image change detection method, said apparatus comprising: processor means adapted to operate in accordance with a predetermined instruction set, said apparatus, in conjunction with said instruction set, being adapted to perform the method steps of aspect eleven of the invention.
  • the present invention also provides computer program products comprising: a computer usable medium having computer readable program code and computer readable system code embodied on said medium for one or more of: monitoring for the alteration or tampering of a detection device; determining a contrast measure for an image; compensating for contrast changes in an image change detection method, within a data processing system, said computer program product comprising computer readable code within said computer usable medium for performing the method steps of any one of aspects nine to eleven of the invention.
  • FIGURE 1 is a functional block diagram of a method of detecting a change of state of the object according to a first embodiment of the invention
  • FIGURE 2 is a functional block diagram detailing the decision module illustrated in Figure 1;
  • FIGURE 3 is a functional block diagram of a method of detecting an object according to a second embodiment of the invention.
  • FIGURE 4 is a functional block diagram depicting in detail the decision block module illustrated in Figure 3;
  • FIGURE 5 is a figurative view of a third embodiment of the invention depicting the effects of change of orientation; and FIGURE 6 is a detailed front view of the invention illustrated in Figure 5.
  • like reference characters designate like or corresponding parts throughout the several views of the drawings.
  • FIG. 1 there is shown a functional block diagram of a system 100 embodying a method for detecting change of state of an object in a sequence of images.
  • the invention is applied to a digital video signal 105 which is comprised of a sequence of individual images or frames which each may be represented as an array of pixels corresponding to measured intensities by a digital CCD camera or alternatively an analogue camera whose output has been further digitised.
  • edge detector module 110 which detects edges of the objects within the image by use of a Sobel filter that has been set with an appropriate threshold. Whilst in this embodiment a Sobel edge filtering function has been used, other edge detection functions such as a Canny filter may be used. As would be appreciated by those skilled in the art, any image processing function which is substantially illumination invariant and hence substantially insensitive to changes in intensity may also be employed.
  • Some illustrative examples of other image processing techniques that may be utilised either individually or in suitable combination include the use of colour information rather than intensity information, since this has less dependence on illumination intensity, the use of a "homomorphic" filtered image, which essentially removes illumination dependence from the scene or the use of a texture measure which will determine the visual texture of the scene in the vicinity of each pixel position.
  • Region masking module 120 allows an operator of the system to select a number of objects within the digital video signal which in turn corresponds to selecting these objects within each frame or image which make up the digital video signal. Typically this will involve selecting those pixels which represents the object including its boundary. In this embodiment, the region masking module 120 allows a user to select all pixels within an arbitrary closed freehand curve, this process being repeated for each set of pixels corresponding to an object. In this way a number of objects may be selected within a given viewing area. In the case of a museum or art gallery monitoring system, the objects selected would correspond to those valuables or artefacts for which an alarm is generated if movement or tampering of the artefact is detected.
  • region masking module 120 For each selected object, region masking module 120 generates a mask 125 and respective masked edges 126 corresponding to a portion and hence a pixel subset of the image which corresponds to each object.
  • masked edge information 126 is those pixels within the masked pixel subset which contain an edge as determined by the Sobel filter applied in the edge detector module 110.
  • the reference edge modeller 130 determines reference edge characteristics or modelled edges 131, to which the edge characteristics of subsequent images can be compared to, the reference edge modeller 130 performs a moving average on masked edge information 126. This involves computing the percentage of time each of the pixels contains an edge during a predetermined learning period. This percentage value is further thresholded, so that for example those pixels which correspond to those defined to have an edge for less than a predetermined percentage of time in the learning period will not form part of the reference edge characteristics or the modelled edges 131 which form an input to decision module 190. This allows an operator to tune the sensitivity of reference edge modeller 130 by varying the threshold value as required.
  • the updating of the reference edge characteristics or modelled edges 131 can be selected by an operator or alternatively these characteristics may be updated automatically according to other changes in the viewing area.
  • the intent of updating the modelled edges 131 is to ensure that a reproducible model of the object being monitored is generated.
  • An automatic process for updating the modelled edges 131 could involve a feedback mechanism to adjust the reference characteristics so that a figure of merit which is fed back to an updater is maintained. This figure of merit could be the number of pixels in the modelled edges 131 for a given object, or the percentage coverage of the object by edge pixels, or the uniformity of that coverage, or alternatively some combination of these factors.
  • a different automatic process, that would not require feedback could use a measure of the visual texture in the image to determine suitable threshold parameters for both the edge detector 110 and reference edge modeller 130.
  • the modelled edges 131 are inputted into the alarm decision processor 190 in the form of those pixels which contain an edge after processing for the particular masked portion of the overall image.
  • AND gate 140 selects only those pixels 141 from the masked edges 126 of subsequent frames which correspond to the pixels of the modeled edges 131 as determined by reference edge modeller 130. In this manner, processing time is reduced as analysis is only performed on the subset of pixels known to contain edges in the modelled edge information 131.
  • This information 141 is also inputted into alarm decision processor 190 along with original mask 121 information.
  • alarm decision processor 190 For every object as determined by mask 121, the ratio of number of pixels which contain an edge of the current image 141 to the reference number of pixels which contain edges 131 is computed and compared to a criterion C in comparison module 191. If the ratio or comparison value "141"/"13I" falls below a predetermined criterion C (i.e. output TRUE) 198 then alarm counter 194 will count the number of subsequent images or frames where this criterion is satisfied. As an example, for a 90 % obscuration limit for an object criterion, C would be set at 10 %. Once alarm counter 194 counts NA images or frames 195 (e.g.
  • an ALARM 196 is generated for that particular object. This feature allows for the object to be totally occluded for a period of time (in this case 10 seconds) before ALARM 196. As would be expected, this is a fairly typical occurrence when people are observing valuables or artefacts in a museum or art gallery.
  • alarm counter 194 is reset.
  • the system can be tuned to determine how much convincing it requires before an object is deemed to be visible again. This may prevent an ALARM 196 occurring, or reset ALARM 196 if it has already occurred. An extension of this is to latch ALARM 196 or record whenever it occurs so that all ALARM 196 events are noted.
  • FIG. 3 there is shown a functional block diagram of a second illustrative embodiment of a system embodying a method for detecting a change of state of an object in a sequence of images 200.
  • This embodiment is similar to that illustrated in Figure 1 with the region masking function 120 (see Figure 1) substantially equivalent to object selection module 210, mask module 220 and AND gate 250.
  • edge detector module 110 (see Figure 1) is substantially equivalent to the combined Sobel filter 230 and associated threshold module 240.
  • the output of AND gate 250 are respective masked edges 251 corresponding to a portion and hence a pixel subset of the image which corresponds to the object selected by selection module 210.
  • a second AND gate corresponding to AND gate 140 is not required due to the use of a Hausdorff distance comparison measure being performed in alarm decision processor 280.
  • I I is some underlying norm on the points of A and B (e.g., the L2, or Euclidean norm).
  • the function h (A, B) is called the directed Hausdorff distance from A to B. It identifies the point a eA that is farthest from any point of B and measures the distance from a to its nearest neighbour in B (using the given norm
  • the edge characteristics of the reference image 271 are compared directly to those of the current image 251.
  • the Hausdorff distance tests how well a model fits the image, as well as how well the image fits the model. Although these two tests seem identical, the following example highlights the importance of considering both aspects.
  • the valuable to be protected is a single, blank sheet of A4 paper. If the user selected a region slightly larger than the piece of paper, the edge model would consist of only four edges, ie the edges of the piece of paper. Now, if this "valuable" was replaced by piece of A4 paper but with a small picture on it, the current image edge map would consist of the four edges of the piece of paper, along with the edges of the picture on the paper. This scenario is similar to a thief stealing an artwork and replacing it with a replica - most of the original content is accounted for, but there are some differences.
  • the reverse partial Hausdorff distance i.e., how well the model fits the image
  • the forward partial Hausdorff distance i.e., how well the image fits the model
  • the forward partial Hausdorff distance would detect that the picture edges were not present in the model. This added ability means that to escape detection, a thief would have to replace the valuable with an exact replica, placed in exactly the same position and orientation.
  • this example serves to define what is meant herein by detecting change of object state, whether that be detecting the actual movement of an object or, determining discrepancies between stored reference images and images of the object being captured under surveillance where, the object may have been tampered with or altered, for example, by way of replacing the object with a replica in an extended time interval between capturing the reference images of the original object and capturing images of the replica object.
  • FIG. 4 there is shown a detailed breakdown of the decision module 280.
  • an extension of the directed Hausdorff distance is used wherein a list of forward and reverse partial Hausdorff distances are calculated and ranked.
  • the forward directed distance h (A, B) instead of calculating the point a which is the maximum distance from a point b in B, calculate the partial Hausdorff distance h a (A, B) for each point a in A and denote the K- th ranked value in this set of distances as h ⁇ (A, B) .
  • Forward distance calculation module 310 determines h 2Oi (A, B) , the K- th ranked value of the forward partial Hausdorff distance corresponding to 20 % of the total number of pixels being compared. This value 311 is inputted to comparison module 330 and if it is greater than 0 a TRUE signal 332 is generated and alarm counter 360 will commence counting frames. This in effect tests whether more than 20 % of the model is present in the image as by definition h 20% (A, B) will be 0 if this is the case.
  • Reverse distance calculation module determines h 6Si (B, A), the K- th ranked value of the reverse partial Hausdorff distance corresponding to 65% of the total number of pixels being compared. This value 321 is inputted into comparison module 330 and if it is greater than 0 a TRUE signal 332 is generated and alarm counter 360 will commence counting frames. Similar to the forward partial distance calculation, this in effect tests whether more than 65 % of the image is in the model as in this case h esi (B, A) will be by definition equal to 0.
  • device 500 for detecting in a given detection region 600 according to another illustrative embodiment of the present invention.
  • device 500 comprises a PIR element 510 operative to detect any infra-red emissions in a field of view whose extent ranges from left boundary 520 (when viewed front on) to rightmost boundary 530 with a view to securing detection region which comprises car park area 600.
  • detection device 500' whose orientation has been changed with respect to correctly aligned device 500 now views a substantially different detection region 600'. Accordingly, the new field of view ranging between left boundary 520' and 530' does not encompass region 540 which corresponds to area 610 of car park 600 not being viewed thereby resulting in this area being insecure. As would be clear to those skilled in the art, the field of views described herein extend in three dimensions having a length, width and depth. Referring now to Figure 6, detection device 500 is illustrated in greater detail. PIR detector 510 may provide an alarm signal if any changes in heat emissions above a predetermined threshold are sensed within the detection regions.
  • PIR detector 510 Mounted below PIR detector 510 is a standard CCD camera 515 which functions to capture an image of the area that substantially agrees with the detection region view by PIR detector 510.
  • any changes in the orientation of PIR detector 510 may result in a different image being viewed by CCD camera 515. Monitoring of this image change results in an alarm signal being generated that indicates that monitoring device 500 has been tampered with.
  • CCD camera 515 is substantially co- aligned with PIR detector 510 to view a similar region this is only one convenient embodiment. Clearly, as long as the orientation of CCD camera 515 remains fixed in relation to that of PIR detector 510, then any tampering with the alignment of PIR detector 510 may be detected by CCD camera 515. Additionally, there may be multiple PIR detectors that are collocated with respect to a single CCD camera 515.
  • Change detection algorithms that are particularly suited to detecting changes in the region viewed by CCD camera 515 have already been described herein with reference to Figures 1 to 4.
  • this algorithm detects changes of an object state within a plurality of sequential images such as would be captured by CCD camera 515.
  • a feature of this change detection algorithm is that it is substantially illumination invariant so that changes in the general lighting of the viewed region do not trigger a false alarm, condition.
  • the change detection algorithms described previously with reference to Figures 1 to 4 are modified by not requiring a user to select a region of interest within a region being viewed. Accordingly, the default behaviour would be to detect if the whole image corresponding to the entire viewing or detection region has changed this further corresponding to movement of monitoring device 500.
  • a user may select a region of interest within the region being viewed which focuses on an object or objects that are known to remain stationary.
  • a further low contrast detector may be included in the algorithm to ensure that the change detection algorithm operates in conditions where there is adequate image contrast.
  • the low contrast detector determines a histogram of the whole image in terms of frequency of pixel intensities for a given intensity bin size or range.
  • the difference between the maximum and minimum intensities for those bins which have a frequency of occurrence above some minimum threshold provides a contrast measure that is substantially insensitive with respect to point sources such as might occur with a generally low contrast region such as a car park at dusk which may have a number of lights operating.
  • the alarm signal provided by the change detection algorithm is ignored or alternatively the change detection algorithm is bypassed.
  • contrast is restored the change detection algorithm resumes normal operation.
  • an alarm signal may be generated once contrast is restored if there has been any tampering with the alignment of device 500.
  • a number of reference images may be stored which correspond to different general lighting conditions. If a comparison between a first stored reference image results in an alarm condition then a further comparison is made with a subsequent reference image corresponding to different lighting conditions. If after this comparison, the alarm condition still exists, then a general alarm is flagged.
  • this use of a number of reference images which each corresponds to a change in the ambient conditions is equally applicable to those embodiments of the present invention which detect the change of an object state from an initial state as described with reference to Figures 1 to 4.
  • this principle may be applied to incorporate any number of reference images and as this comparison may be made essentially instantaneously this does not add significantly to the real time performance of the change detection algorithm.
  • the storing of these reference images would be incorporated into the setup of device 500.
  • a CCD camera and associated change detection algorithm are employed to monitor the change of detecting direction of device 500 clearly other tamper monitoring means are contemplated to be within the scope of the invention.
  • One example comprises a collimated detector incorporated with device 500 which detects emitted light from an alignment laser. If the laser is no longer detected this would imply that the detector is no longer in line with the laser and hence the orientation of device 500 has changed.
  • Another example of a suitable monitoring device would be an Inertial Measurement Unit (IMU) fixedly located with respect to device 500 which would directly measure the geospatial orientation and provide an alarm signal corresponding to tampering when the orientation changes.
  • IMU Inertial Measurement Unit
  • the CCD camera may form both the detector which views the detection region and the tamper monitoring means which determines any changes in the viewing direction of the detector. Separate algorithms based on the image processing methods described herein or otherwise would then be employed to process the raw output image data from the CCD camera.
  • a first "tamper monitoring" algorithm is tailored to detect those changes which correspond to a change of viewing direction of the detector, for example by concentrating on a fixed object of known orientation.
  • a second separate algorithm would then be customised to determine if an object of interest is missing from the detection region.
  • the CCD camera may simply record and store the images for later review by security personnel with an alarm only being generated when a change of the viewing direction of the detector has been determined by the "tamper monitoring" algorithm.
  • object scene may comprise a region of interest in a field of view containing an object such as a valuable, for example, a painting in a museum;
  • object image feature may comprise intensity or some other image attributes etc but most preferably object edges;
  • predetermined comparison metric may comprise logical AND or preferably the "Hausdorff Distance" in the preferred embodiment using image edges;
  • portion does not necessarily correspond to the
  • a “portion” can be any part of the image.
  • a “portion” could also be expressed as a subset of the pixels of a recorded image.
  • means-plus-function clauses are intended to cover structures as performing the defined function and not only structural equivalents, but also equivalent structures.
  • a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface to secure wooden parts together, in the environment of fastening wooden parts, a nail and a screw are equivalent structures.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Security & Cryptography (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

L'invention porte sur un procédé et un appareil de détection des modifications de l'état d'un objet à partir d'un état initial où l'objet est figuré par une suite d'images. Le système consiste à comparer une mesure effectuée sur une partie prédéterminée de chacune des images correspondant à l'état initial de l'objet avec une valeur de mesure de référence calculée lorsque l'objet est à l'état initial, afin d'établir une valeur de comparaison pour chacune des images, puis à produire un signal indiquant que l'état de l'objet a changé lorsqu'un nombre prédéterminé de valeurs de comparaison correspondant à chacune des images ne satisfait pas à un critère prédéterminé.
PCT/AU2005/000955 2004-06-30 2005-06-30 Appareil et procede de traitement d'images WO2006002466A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11/571,476 US8295541B2 (en) 2004-06-30 2005-06-30 System and method for detecting a change in an object scene

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
AU2004903572A AU2004903572A0 (en) 2004-06-30 Image processing method and apparatus
AU2004903572 2004-06-30
AU2004904053A AU2004904053A0 (en) 2004-07-23 Image processing method and apparatus
AU2004904053 2004-07-23
AU2004906689 2004-11-23
AU2004906689A AU2004906689A0 (en) 2004-11-23 Detection system
US64260805P 2005-01-10 2005-01-10
US60/642,608 2005-01-10

Publications (1)

Publication Number Publication Date
WO2006002466A1 true WO2006002466A1 (fr) 2006-01-12

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2005/000955 WO2006002466A1 (fr) 2004-06-30 2005-06-30 Appareil et procede de traitement d'images

Country Status (1)

Country Link
WO (1) WO2006002466A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8073261B2 (en) * 2006-12-20 2011-12-06 Axis Ab Camera tampering detection
EP4205095A4 (fr) * 2020-09-30 2024-11-13 Trackonomy Systems, Inc. Système et procédé de détection par noeuds intelligents d'événements pour la protection et la sécurité à la frontière

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GB2045493A (en) * 1979-02-13 1980-10-29 Birkett A K A trigger circuit and method for detecting changes in light levels in the field of scan of a camera
GB2150724A (en) * 1983-11-02 1985-07-03 Christopher Hall Surveillance system
US4922093A (en) * 1987-06-05 1990-05-01 Bertin & Cie Method and a device for determining the number of people present in a determined space by processing the grey levels of points in an image
EP0261917B1 (fr) * 1986-09-20 1994-03-02 Sony Corporation Détection de changements dans des données vidéo
GB2282294A (en) * 1993-09-25 1995-03-29 Bosch Gmbh Robert Method of detecting moving objects
JPH08265740A (ja) * 1995-03-20 1996-10-11 Fujitsu General Ltd カメラ監視装置
GB2308260A (en) * 1995-12-14 1997-06-18 Alec Moses Messulam Video recording equipment
WO1999023626A1 (fr) * 1997-11-03 1999-05-14 Telediffusion De France Procede d'analyse d'image video pour camera fixe
WO2002032129A1 (fr) * 2000-10-13 2002-04-18 Comtrak Technologies, Llc Procede de recherche de secteurs d'activite dans des enregistrements video
WO2003001467A1 (fr) * 2001-06-25 2003-01-03 Wespot Ab Procede et dispositif de detection de mouvement
WO2004079681A1 (fr) * 2003-03-07 2004-09-16 Quality Labs. Corporation Unite de suivi

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2617112A1 (de) * 1976-04-17 1977-10-27 Bosch Gmbh Robert System zum feststellen einer bewegung im ueberwachungsbereich einer fernsehkamera
GB2045493A (en) * 1979-02-13 1980-10-29 Birkett A K A trigger circuit and method for detecting changes in light levels in the field of scan of a camera
GB2150724A (en) * 1983-11-02 1985-07-03 Christopher Hall Surveillance system
EP0261917B1 (fr) * 1986-09-20 1994-03-02 Sony Corporation Détection de changements dans des données vidéo
US4922093A (en) * 1987-06-05 1990-05-01 Bertin & Cie Method and a device for determining the number of people present in a determined space by processing the grey levels of points in an image
GB2282294A (en) * 1993-09-25 1995-03-29 Bosch Gmbh Robert Method of detecting moving objects
JPH08265740A (ja) * 1995-03-20 1996-10-11 Fujitsu General Ltd カメラ監視装置
GB2308260A (en) * 1995-12-14 1997-06-18 Alec Moses Messulam Video recording equipment
WO1999023626A1 (fr) * 1997-11-03 1999-05-14 Telediffusion De France Procede d'analyse d'image video pour camera fixe
WO2002032129A1 (fr) * 2000-10-13 2002-04-18 Comtrak Technologies, Llc Procede de recherche de secteurs d'activite dans des enregistrements video
WO2003001467A1 (fr) * 2001-06-25 2003-01-03 Wespot Ab Procede et dispositif de detection de mouvement
WO2004079681A1 (fr) * 2003-03-07 2004-09-16 Quality Labs. Corporation Unite de suivi

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DATABASE WPI Week 199718, Derwent World Patents Index; Class W02, AN 1997-195893 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8073261B2 (en) * 2006-12-20 2011-12-06 Axis Ab Camera tampering detection
EP4205095A4 (fr) * 2020-09-30 2024-11-13 Trackonomy Systems, Inc. Système et procédé de détection par noeuds intelligents d'événements pour la protection et la sécurité à la frontière

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