Multi-object tracking for road surveillance without using features of image data
Multi-object tracking for road surveillance without using features of image data
复制标题
不使用图像数据特征的道路监控多目标跟踪
DOI：
10.1109/globecom46510.2021.9686010
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
E. Oki
中科院分区：
文献类型：
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作者：
N. Kishi;R. Shinkuma;M. Oka;T. Sato;E. Oki
为了保证人们的道路安全，已经开发了对道路上的动态对象的视觉监视。特别是，车辆跟踪被认为是道路安全的关键技术，多目标跟踪（MOT）的研究正在积极进行。然而，当进行MOT时，由于系统的技术限制或隐私问题，原始视觉数据并不总是可用的; MOT需要仅使用从对象检测器获得的坐标来执行，而不使用从原始图像数据中提取的特征，例如车辆的颜色，这将MOT的准确性降低到道路安全的不令人满意的水平。本文提出了一种移动车辆的MOT方案，其灵感来自于使用Viterbi算法的小区跟踪。该方案扩展了布朗运动模型，这是用于细胞跟踪的基础计划，根据车辆在道路上的行驶方向加权概率转换。我们评估所提出的计划，使用模拟车辆交通数据，并验证所提出的计划比基准计划的MOT的准确性。我们还演示了一个例子，所提出的计划如何工作以及真实的车辆交通数据。
Visual surveillance of dynamic objects on roads has been developed to ensure road safety for people. Particularly, vehicle tracking is considered as a key technology for the road safety; studies on multi-object tracking (MOT) are being actively pursued. However, when MOT is performed, raw vision data are not always available because of the technical limitation or the privacy concern of the system; MOT needs to be performed only using the coordinates obtained from the object detector without using features extracted from raw image data such as color of vehicles, which degrades the accuracy of MOT to the unsatisfactory level for road safety. This paper proposes an MOT scheme for moving vehicles that is inspired by cell tracking using the Viterbi algorithm. The proposed scheme extends the Brownian motion model, which was used in the base scheme of cell tracking, by weighting probability transitions in accordance with the direction of travel of vehicles on the road. We evaluate the proposed scheme using simulated vehicle-traffic data and verify that the proposed scheme performs better than benchmark schemes in terms of the accuracy of MOT. We also demonstrate an example of how the proposed scheme works well for real vehicle-traffic data.