Abstract
Multi-object tracking (MOT) is an important and representative task in the field of computer vision, while tracking-by-detection is the most mainstream paradigm for MOT, so that target detection quality, feature representation ability, and association algorithm greatly affect tracking performance. On the one hand, multiple pedestrians moving together in the same group maintain similar motion pattern, so that they can indicate each other’s moving state. We extract groups from detections and maintain the group relationship of trajectories in tracking. We propose a state transition mechanism to smooth detection bias, recover missing detection and confront false detection. We also build a two-level group-detection association algorithm, which improves the accuracy of association. On the other hand, different areas of the tracking scene have diverse and varying impact on the detections’ appearance feature, which weakens the appearance feature’s representation ability. We propose a self-adaptive feature fusion strategy based on the tracking scene and the group structure, which can help us to get fusion feature with stronger representative ability to use in the trajectory-detection association to improve tracking performance. To summary, in this paper, we propose a novel Group Perception based Self-adaptive Fusion Tracking (GST) framework, including Group concept and Group Exploration Net, Group Perception based State Transition Mechanism, and Self-adaptive Feature Fusion Strategy. Experiments on the MOT17 dataset demonstrate the effectiveness of our method. The method achieves competitive results compared to the state-of-the-art methods.
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Acknowledgements
This study is partially supported by the National Key R &D Program of China (No.2022YFB3306500), the National Natural Science Foundation of China (No.61872025). Thanks for the support from HAWKEYE Group.
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Xing, Y. et al. (2024). Group Perception Based Self-adaptive Fusion Tracking. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_8
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