Abstract
Multi-Object Tracking (MOT) methods within Tracking-by-Detection paradigm are usually modeled as graph problem. It is challenging to associate objects in dense scenes with frequent occlusion. To further model object interactions and repair detection errors, we use graph network to extract embeddings for data association. Graph neural network makes it possible for embeddings aggregate and update between vertices (detections and trajectories). We both introduce priori confidence to detection attention and trajectory attention, which consider the interaction between occluded objects in the same frame. Based on MHT framework, we train two graph networks for clustering in adjacent frame and association between long spaced tracklets. Experiments on MOT17/20 benchmarks demonstrate the significant improving in tracking accuracy of proposed method and show state-of-the-art performance for MOT with public detections.
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Acknowledgements
This study is partially supported by the National Key R &D Program of China (No. 2018YFB2101100), the National Natural Science Foundation of China (No. 61872025), and the Science and Technology Development Fund, Macau SAR (File no.0001/2018/AFJ) and the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2021ZX-03). Thank you for the support from HAWKEYE Group.
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Wu, Y., Sheng, H., Wang, S., Liu, Y., Ke, W., Xiong, Z. (2022). Data Association with Graph Network for Multi-Object Tracking. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_21
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