Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 7, Pages 4178-4188Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2897128
Keywords
Edge artificial intelligence; multiobject tracking (MOT); rank-based dynamic tracklet
Categories
Funding
- China Postdoctoral Science Foundation [2018M640950]
- Fundamental Research Funds for the Central Universities [GK201903103, 21618329]
- NSFC [11871248, 11772178, 11872036, 61877037, 61703115]
- National Key Research and Development Program of China [2017YFB1402102]
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Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multiobject tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering that data association optimization and association affinity model are two key parts in MOT, an online multipedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank-based dynamic motion affinity model. The rank-based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.
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