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Robust hierarchical multiple hypothesis tracker for multiple-object tracking

机译：鲁棒的分层多假设跟踪器，用于多目标跟踪

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摘要

Multiple object tracking is a fundamental subsystem of many higher level applications such as traffic monitoring, people counting, robotic vision and many more. This paper explains in details the methodology of building a robust hierarchical multiple hypothesis tracker for tracking multiple objects in the videos. The main novelties of our approach are anchor-based track initialization, prediction assistance for unconfirmed track and two virtual measurements for confirmed track. The system is built mainly to deal with the problems of merge, split, fragments and occlusion. The system is divided into two levels where the first level obtains the measurement input from foreground segmentation and clustered optical flow. Only K-best hypothesis and one-to-one association are considered. Two more virtual measurements are constructed to help track retention rate for the second level, which are based on predicted state and division of occluded foreground segments. Track based K-best hypothesis with multiple associations are considered for more comprehensive observation assignment. Histogram intersection testing is performed to limit the tracker bounding box expansion. Simulation results show that all our algorithms perform well in the surroundings mentioned above. Two performance metrics are used; multiple-object tracking accuracy (MOTA) and multiple-object tracking precision (MOTP). Our tracker have performed the best compared to the benchmark trackers in both performance evaluation metrics. The main weakness of our algorithms is the heavy processing requirement.
机译：多对象跟踪是许多高级应用程序（例如交通监控，人员计数，机器人视觉等）的基本子系统。本文详细说明了构建健壮的分层多假设跟踪器以跟踪视频中多个对象的方法。我们方法的主要新颖之处在于基于锚的航迹初始化，对未确认航迹的预测辅助以及对于已确认航迹的两个虚拟测量。该系统主要用于处理合并，拆分，碎片和遮挡的问题。该系统分为两个级别，其中第一个级别从前景分割和群集光流中获取测量输入。仅考虑K最佳假设和一对一关联。基于预测的状态和被遮挡的前景片段的划分，还构建了两个虚拟测量来帮助跟踪第二级的保留率。为了更全面地进行观察分配，考虑了具有多个关联的基于轨迹的K最优假设。进行直方图相交测试以限制跟踪器边界框扩展。仿真结果表明，我们所有的算法在上述环境中均表现良好。使用了两个性能指标；多目标跟踪精度（MOTA）和多目标跟踪精度（MOTP）。在两个性能评估指标中，我们的跟踪器都与基准跟踪器相比表现最好。我们算法的主要缺点是处理要求高。

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