ELECTRICITY: An Efficient Multi-Camera Vehicle Tracking System for Intelligent City

Yijun Qian, Lijun Yu, Wenhe Liu, Alexander G. Hauptmann; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 588-589

City-scale multi-camera vehicle tracking is an important task in the intelligent city and traffic management. It is quite challenging with large scale variance, frequent occlusion and appearance variance caused by viewing perspective difference. In this paper, we propose ELECTRICITY, an efficient multi-camera vehicle tracking system with aggregation loss and fast multi-target cross-camera tracking strategy. The proposed system contains four main modules. Firstly, we extract tracklets under a single camera view through object detection and multi-object tracking modules which shared the detection features. After that, we match the generated tracklets through a multi-camera re-identification module. Finally, we eliminate isolated tracklets and synchronize tracking ids according to the re-identification results. The proposed system wins the first place in the City-Scale Multi-Camera Vehicle Tracking of AI City 2020 Challenge (Track 3) with a score of 0.4585.