一、摘要

This paper focuses on the Multi-Target Multi-Camera Tracking task (MTMCT), which aims at tracking multiple targets within a multi-camera network. As the trajectory of each target is inherently split into multiple sub-trajectories (namely local tracklets) in a multi-camera network, a major challenge of MTMCT is how to accurately match the local tracklets generated within each camera across different cameras and generate a complete global trajectory for each target, i.e., t h e cross-camera tracklet matching problem. We solve the cross-camera tracklet matching problem by TRACklet-to-Target Assignment (TRACTA), and propose the Restricted Non-negative Matrix Factorization (RNMF) algorithm to compute the optimal assignment solution that meets a set of constraints, which should be in force in practice. TRACTA can correct the tracking errors caused by occlusions and missed detections in local tracklets, and produce a complete global trajectory for each target across all the cameras. Moreover, we also develop an analytical way of estimating the total number of targets in the camera network, which plays an important role to compute the tracklet-to-target assignment. Experimental evaluations and ablation studies on four MTMCT benchmark datasets show the superiority of the proposed TRACTA method.

由于在多摄像机网络中，每个目标的轨迹都有可能分裂为多个子轨迹(即局部轨迹)，MTMCT的主要挑战是如何在不同摄像机之间精确匹配每个摄像机内生成的局部轨迹，并为每个目标生成完整的全局轨迹，即跨摄像机的轨迹匹配问题
本文通过**tracklet -to- target Assignment (TRACTA)方法解决了跨摄像机的tracklet匹配问题，并提出了受限非负矩阵分解(Restricted Non-negative Matrix Factorization，RNMF)**算法来计算满足一组约束条件的最优分配解，该算法在实际应用中应该是有效的。
TRACTA 可以纠正局部轨迹（单个摄像机中）中由于遮挡和漏检引起的跟踪误差，并在所有摄像机上为每个目标生成完整的全局轨迹。