Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019

Yucheng Chen, Longlong Jing, Elahe Vahdani, Ling Zhang, Mingyi He, Yingli Tian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 324-332

Abstract


In this work, we present our solutions to the image-based vehicle re-identification (ReID) track and multi-camera vehicle tracking (MVT) tracks on AI City Challenge 2019 (AIC2019). For the ReID track, we propose an enhanced multi-granularity network with multiple branches to extract visual features for vehicles with different levels of grains. With the help of these multi-grained features, the proposed framework outperforms the current state-of-the-art vehicle ReID method by 16.3% on Veri dataset. For the MVT track, we first generate tracklets by Kernighan-Lin graph partitioning algorithm with feature and motion correlation, then combine tracklets to trajectories by proposed progressive connection strategy, finally match trajectories under different camera views based on the annotated road boundaries. Our MVT and ReID algorithms are ranked the 10 and 23 in MVT and ReID tracks respectively at the NVIDIA AI City Challenge 2019.

Related Material


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[bibtex]
@InProceedings{Chen_2019_CVPR_Workshops,
author = {Chen, Yucheng and Jing, Longlong and Vahdani, Elahe and Zhang, Ling and He, Mingyi and Tian, Yingli},
title = {Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}