Multi-Camera Vehicle Tracking System for AI City Challenge 2022
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 track-let 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.
Cite
Text
Li et al. "Multi-Camera Vehicle Tracking System for AI City Challenge 2022." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00369Markdown
[Li et al. "Multi-Camera Vehicle Tracking System for AI City Challenge 2022." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/li2022cvprw-multicamera/) doi:10.1109/CVPRW56347.2022.00369BibTeX
@inproceedings{li2022cvprw-multicamera,
title = {{Multi-Camera Vehicle Tracking System for AI City Challenge 2022}},
author = {Li, Fei and Wang, Zhen and Nie, Ding and Zhang, Shiyi and Jiang, Xingqun and Zhao, Xingxing and Hu, Peng},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {3264-3272},
doi = {10.1109/CVPRW56347.2022.00369},
url = {https://mlanthology.org/cvprw/2022/li2022cvprw-multicamera/}
}