School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Korea.
Sensors (Basel). 2022 Jul 14;22(14):5267. doi: 10.3390/s22145267.
Multi-Target Multi-Camera Tracking (MTMCT), which aims to track multiple targets within a multi-camera network, has recently attracted considerable attention due to its wide range of applications. The main challenge of MTMCT is to match local tracklets (i.e., sub-trajectories) obtained by different cameras and to combine them into global trajectories across the multi-camera network. This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs), such as indoor multi-camera environments. We present a new lightweight matching method for the MTMC task that employs similarity analysis for location features. The proposed approach comprises two steps: (i) extracting the motion information of targets based on a ground projection method and (ii) matching the tracklets using similarity analysis based on the Dynamic Time Warping (DTW) algorithm. We use a Kanade-Lucas-Tomasi (KLT) algorithm-based frame-skipping method to reduce the computational overhead in object detection and to produce a smooth estimate of the target's local tracklets. To improve matching accuracy, we also investigate three different location features to determine the most appropriate feature for similarity analysis. The effectiveness of the proposed method has been evaluated through real experiments, demonstrating its ability to accurately match local tracklets.
多目标多摄像机跟踪 (MTMCT) 旨在跟踪多摄像机网络中的多个目标，由于其广泛的应用，最近引起了相当多的关注。MTMCT 的主要挑战是匹配由不同摄像机获得的局部轨迹段（即子轨迹），并将它们组合成跨多摄像机网络的全局轨迹。本文针对部分重叠视场 (FOV) 场景中的摄像机间轨迹段匹配问题进行了研究，例如室内多摄像机环境。我们提出了一种新的轻量级 MTMC 任务匹配方法，该方法采用位置特征的相似性分析。所提出的方法包括两个步骤：（i）基于地面投影方法提取目标的运动信息；（ii）使用基于动态时间规整 (DTW) 算法的相似性分析来匹配轨迹段。我们使用基于 Kanade-Lucas-Tomasi (KLT) 算法的帧跳方法来减少目标检测的计算开销，并生成目标局部轨迹段的平滑估计。为了提高匹配精度，我们还研究了三种不同的位置特征，以确定最适合相似性分析的特征。通过真实实验评估了所提出方法的有效性，证明了其能够准确匹配局部轨迹段的能力。
