A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling
This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total numbe...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
IEEE COMPUTER SOC
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/90801 |
| _version_ | 1848765432122572800 |
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| author | Ong, Jonah Vo, Ba Tuong Vo, Ba-Ngu Kim, Du Yong Nordholm, Sven |
| author_facet | Ong, Jonah Vo, Ba Tuong Vo, Ba-Ngu Kim, Du Yong Nordholm, Sven |
| author_sort | Ong, Jonah |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset. |
| first_indexed | 2025-11-14T11:35:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-90801 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:09Z |
| publishDate | 2022 |
| publisher | IEEE COMPUTER SOC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-908012023-04-20T05:37:01Z A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling Ong, Jonah Vo, Ba Tuong Vo, Ba-Ngu Kim, Du Yong Nordholm, Sven Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering Three-dimensional displays Cameras Trajectory Bayes methods Detectors Training Visualization Multi-view multi-sensor multi-object visual tracking occlusion handling generalized labeled multi-bernoulli PERFORMANCE EVALUATION MULTITARGET TRACKING VISUAL TRACKING CAMERAS This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset. 2022 Journal Article http://hdl.handle.net/20.500.11937/90801 10.1109/TPAMI.2020.3034435 English http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP160104662 http://creativecommons.org/licenses/by/4.0/ IEEE COMPUTER SOC fulltext |
| spellingShingle | Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering Three-dimensional displays Cameras Trajectory Bayes methods Detectors Training Visualization Multi-view multi-sensor multi-object visual tracking occlusion handling generalized labeled multi-bernoulli PERFORMANCE EVALUATION MULTITARGET TRACKING VISUAL TRACKING CAMERAS Ong, Jonah Vo, Ba Tuong Vo, Ba-Ngu Kim, Du Yong Nordholm, Sven A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title | A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title_full | A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title_fullStr | A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title_full_unstemmed | A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title_short | A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling |
| title_sort | bayesian filter for multi-view 3d multi-object tracking with occlusion handling |
| topic | Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering Three-dimensional displays Cameras Trajectory Bayes methods Detectors Training Visualization Multi-view multi-sensor multi-object visual tracking occlusion handling generalized labeled multi-bernoulli PERFORMANCE EVALUATION MULTITARGET TRACKING VISUAL TRACKING CAMERAS |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/90801 |