Multi-Camera Vehicle Tracking System for AI City Challenge 2022

Fei Li, Zhen Wang, Ding Nie, Shiyi Zhang, Xingqun Jiang, Xingxing Zhao, Peng Hu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 3265-3273

Abstract

Multi-Target Multi-Camera tracking is a fundamental task for intelligent traffic systems. The track 1 of AI City Challenge 2022 aims at the city-scale multi-camera vehicle tracking task. In this paper we propose an accurate vehicle tracking system composed of 4 parts, including: (1) State-of-the-art detection and re-identification models for vehicle detection and feature extraction. (2) Single camera tracking, where we introduce augmented tracks prediction and multi-level association method on top of tracking-by-detection paradigm.(3) Zone-based singe-camera tracklet merging strategy. (4) Multi-camera spatial-temporal matching and clustering strategy. The proposed system achieves promising results and ranks the second place in Track 1 of the AI City Challenge 2022 with a IDF1 score of 0.8437.

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[bibtex]
@InProceedings{Li_2022_CVPR, author = {Li, Fei and Wang, Zhen and Nie, Ding and Zhang, Shiyi and Jiang, Xingqun and Zhao, Xingxing and Hu, Peng}, title = {Multi-Camera Vehicle Tracking System for AI City Challenge 2022}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {3265-3273} }