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. , the 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.

中文翻译：

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通过小轨迹到目标分配进行多目标多摄像机跟踪

本文重点介绍多目标多摄像机跟踪任务（MTMCT），该任务旨在跟踪多摄像机网络中的多个目标。由于在多摄像机网络中每个目标的轨迹都固有地分为多个子轨迹（即局部轨迹），因此MTMCT的主要挑战是如何准确地将跨不同摄像机的每个摄像机中生成的局部轨迹匹配起来，并生成完整的每个目标的全球轨迹，即 ， 跨相机小径匹配问题。我们通过TRACklet到目标分配（TRACTA）解决了跨相机小径匹配问题，并提出了受限非负矩阵分解（RNMF）算法来计算满足一组约束的最优分配解决方案，该解决方案应在在实践中发挥作用。TRACTA可以纠正由于局部轨迹中的遮挡和漏检而导致的跟踪错误，并为所有摄像机上的每个目标生成完整的全局轨迹。此外，我们还开发了一种估计相机网络中目标总数的分析方法，该方法在计算轨迹到目标的分配中起着重要作用。对四个MTMCT基准数据集的实验评估和消融研究表明，所提出的TRACTA方法具有优越性。