Robust and Online Vehicle Counting at Crowded Intersections

Jincheng Lu, Meng Xia, Xu Gao, Xipeng Yang, Tianran Tao, Hao Meng, Wei Zhang, Xiao Tan, Yifeng Shi, Guanbin Li, Errui Ding; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 4002-4008

Abstract

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowed intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.

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[bibtex]
@InProceedings{Lu_2021_CVPR, author = {Lu, Jincheng and Xia, Meng and Gao, Xu and Yang, Xipeng and Tao, Tianran and Meng, Hao and Zhang, Wei and Tan, Xiao and Shi, Yifeng and Li, Guanbin and Ding, Errui}, title = {Robust and Online Vehicle Counting at Crowded Intersections}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {4002-4008} }