Mondal, 2019 - Google Patents
Neuro-probabilistic model for object trackingMondal, 2019
View PDF- Document ID
- 729840544596936324
- Author
- Mondal A
- Publication year
- Publication venue
- Pattern Analysis and Applications
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Snippet
Occlusion is one of the major challenges for object tracking in real-life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues, namely motion model and observation (ie, likelihood) …
- 239000002245 particle 0 abstract description 48
Classifications
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
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- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
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- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
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- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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