作者：Yuhang He, Jie Han, Wentao Yu, Xiaopeng Hong∗, Xing Wei, and Yihong Gong

**作者单位：**西安交通大学电子与信息工程学院

发布时间：2020

发布期刊/会议：CVPRW

出版商：IEEE

**论文全称：**City-Scale Multi-Camera Vehicle Tracking by Semantic Attribute Parsing and Cross-Camera Tracklet Matching

论文地址：

论文代码：

地位：2020 AI-CITY 挑战赛第二名

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一、摘要

This paper focuses on the Multi-Target Multi-Camera Tracking (MTMCT) task in a city-scale multi-camera network. As the trajectory of each target is naturally split into multiple sub-trajectories (namely local tracklets) in different cameras, the key issue of MTMCT is how to match local tracklets belonging to the same target across different cameras. To this end, we propose an efficient two-step MTMCT approach to robustly track vehicles in a camera network. It first generates all local tracklets and then matches the ones belonging to the same target across different cameras. More specifically, in the local tracklet generation phase, we follow the tracking-by-detection paradigm and link the detections to local tracklets by graph clustering. In the cross-camera tracklet matching phase, we first develop a spatial-temporal attention mechanism to produce robust tracklet representations. We then prune false matching candidates by traffic topology reasoning and match tracklets across cameras using the recently proposed TRACklet-to-Target Assignment (TRACTA) algorithm. The proposed method is evaluated on the City-Scale Multi-Camera Vehicle Tracking task at the 2020 AI City Challenge and achieves the second-best results.

• 由于每个目标的轨迹在不同的摄像机中被自然地分割为多个子轨迹(即局部轨迹)，MTMCT的关键问题是如何在不同的摄像机中匹配属于同一目标的局部轨迹。