Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models

Hung-Min Hsu, Tsung-Wei Huang, Gaoang Wang, Jiarui Cai, Zhichao Lei, Jenq-Neng Hwang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 416-424

Due to the exponential growth of traffic camera networks, the need for multi-camera tracking (MCT) for intelligent transportation has received more and more attention. The challenges of MCT include similar vehicle models, significant feature variation in different orientations, color variation of the same car due to lighting conditions, small object sizes and frequent occlusion, as well as the varied resolutions of videos. In this work, we propose an MCT system, which combines single-camera tracking (SCT) and inter-camera tracking (ICT) which includes trajectory-based camera link model and deep feature reidentification. For SCT, we use a TrackletNet Tracker (TNT), which effectively generates the moving trajectories of all detected vehicles by exploiting temporal and appearance information of multiple tracklets that are created by associating bounding boxes of detected vehicles. The tracklets are generated based on CNN feature matching and intersection-over-union (IOU) in every single-camera view. In terms of deep feature re-identification, we exploit the temporal attention model to extract the most discriminant feature of each trajectory. In addition, we propose the trajectory-based camera link models with order constraint to efficiently leverage the spatial and temporal information for ICT. The proposed method is evaluated on CVPR AI City Challenge2019 City Flow dataset, achieving IDF1 70.59%, which outperforms competing methods.