View adaptive multi-object tracking method based on depth relationship cues
Abstract Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differenc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01776-7 |
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| _version_ | 1823861509904662528 |
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| author | Haoran Sun Yang Li Guanci Yang Zhidong Su Kexin Luo |
| author_facet | Haoran Sun Yang Li Guanci Yang Zhidong Su Kexin Luo |
| author_sort | Haoran Sun |
| collection | DOAJ |
| description | Abstract Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed View Adaptive Multi-Object Tracking Method Based on Depth Relationship Cues (ViewTrack) to enable MOT to adapt to the scene's dynamic changes. Firstly, based on exploiting the depth relationships between objects by using the position information of the bounding box, a view-type recognition method based on depth relationship cues (VTRM) is proposed to perceive the changes of depth and view within the dynamic scene. Secondly, by adjusting the interval partitioning strategy to adapt to the changes in view characteristics, a view adaptive partitioning method for tracklet sets and detection sets (VAPM) is proposed to achieve sparse decomposition in occluded scenes. Then, combining pedestrian displacement with Intersection over Union (IoU), a displacement modulated Intersection over Union method (DMIoU) is proposed to improve the association accuracy between detection and tracklet boxes. Finally, the comparison results with 12 representative methods demonstrate that ViewTrack outperforms multiple metrics on the benchmark datasets. The code is available at https://github.com/Hamor404/ViewTrack . |
| format | Article |
| id | doaj-art-dcc6ad246a354edab8edd79961d00860 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-dcc6ad246a354edab8edd79961d008602025-02-09T13:01:00ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111212110.1007/s40747-024-01776-7View adaptive multi-object tracking method based on depth relationship cuesHaoran Sun0Yang Li1Guanci Yang2Zhidong Su3Kexin Luo4Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversitySchool of Engineering, Colorado State University PuebloKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou UniversityAbstract Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed View Adaptive Multi-Object Tracking Method Based on Depth Relationship Cues (ViewTrack) to enable MOT to adapt to the scene's dynamic changes. Firstly, based on exploiting the depth relationships between objects by using the position information of the bounding box, a view-type recognition method based on depth relationship cues (VTRM) is proposed to perceive the changes of depth and view within the dynamic scene. Secondly, by adjusting the interval partitioning strategy to adapt to the changes in view characteristics, a view adaptive partitioning method for tracklet sets and detection sets (VAPM) is proposed to achieve sparse decomposition in occluded scenes. Then, combining pedestrian displacement with Intersection over Union (IoU), a displacement modulated Intersection over Union method (DMIoU) is proposed to improve the association accuracy between detection and tracklet boxes. Finally, the comparison results with 12 representative methods demonstrate that ViewTrack outperforms multiple metrics on the benchmark datasets. The code is available at https://github.com/Hamor404/ViewTrack .https://doi.org/10.1007/s40747-024-01776-7Multi-object trackingTracking-by-detectionView adaptiveDepth relationshipData association |
| spellingShingle | Haoran Sun Yang Li Guanci Yang Zhidong Su Kexin Luo View adaptive multi-object tracking method based on depth relationship cues Complex & Intelligent Systems Multi-object tracking Tracking-by-detection View adaptive Depth relationship Data association |
| title | View adaptive multi-object tracking method based on depth relationship cues |
| title_full | View adaptive multi-object tracking method based on depth relationship cues |
| title_fullStr | View adaptive multi-object tracking method based on depth relationship cues |
| title_full_unstemmed | View adaptive multi-object tracking method based on depth relationship cues |
| title_short | View adaptive multi-object tracking method based on depth relationship cues |
| title_sort | view adaptive multi object tracking method based on depth relationship cues |
| topic | Multi-object tracking Tracking-by-detection View adaptive Depth relationship Data association |
| url | https://doi.org/10.1007/s40747-024-01776-7 |
| work_keys_str_mv | AT haoransun viewadaptivemultiobjecttrackingmethodbasedondepthrelationshipcues AT yangli viewadaptivemultiobjecttrackingmethodbasedondepthrelationshipcues AT guanciyang viewadaptivemultiobjecttrackingmethodbasedondepthrelationshipcues AT zhidongsu viewadaptivemultiobjecttrackingmethodbasedondepthrelationshipcues AT kexinluo viewadaptivemultiobjecttrackingmethodbasedondepthrelationshipcues |