作者：Hung-Min Hsu, Tsung-Wei Huang, Gaoang Wang, Jiarui Cai, Zhichao Lei, Jenq-Neng Hwang
作者单位：华盛顿大学电子与计算机工程系**, 台湾中央研究院**资讯科技创新研究中心
发布时间：2019
发布期刊/会议：CVPRW
出版商：IEEE
**论文全称：**Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models
论文地址：
CVPR 2019 Open Access Repository
论文代码：
地位：在CVPR AI City Challenge2019城市流数据集上进行了评估，IDF1达到70.59%，获得第一名。
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.