Multi-Camera Vehicle Tracking with Powerful Visual Features and Spatial-Temporal Cue

Zhiqun He, Yu Lei, Shuai Bai, Wei Wu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 203-212

Vehicle re-identification and multi-camera multi-object vehicle tracking are important components in the field of intelligent traffic, which is attracting more and more atten- tion. In the NVIDIA AI City Challenge, we propose our solutions to solve this issues. In Track1 task, clustering loss and trajectory consistent loss are introduced into the vehicle re-identification training framework to train more suitable trajectory-based features for cluster task. Besides, spatial- temporal cue is fully excavated to make up the deficiency of appearance feature and constrained hierarchical clus- tering is introduced into the pipeline to get the final clus- ter results. In Track2 task, we propose an effective vehicle training framework and trajectory-based weighted ranking method, which greatly improve the performance. Further- more, an efficient way to mining the additional data to train more robust feature is proposed to enlarge the training data. Finally, our algorithm achieves the state-of-the-art perfor- mance in the competition.