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Bhattacharyya distance-based tracking: A vehicle counting application

机译:Bhattacharyya基于距离的跟踪:车辆计数应用程序

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Vehicle growth in Indonesia is not supported by the number of the road. This fact has caused traffic congestion easily occurred, especially in big cities. Intelligent Transportation System (ITS) is one of the solutions for this problem. In ITS, vehicle tracking and counting is a challenging issue for traffic surveillance. In this paper, Bhattacharyya distance-based tracking and vehicle counting is proposed. We propose a vehicle tracking method and vehicle counting system. We use Adaptive Background Learning with Hole Filling Algorithm to separate background and foreground in a frame. We detect objects in foreground region using contour of each object. Each detected object tracked using Bhattacharyya distance. Each tracked object is counted when the tracked object passed a predefined counting line. The proposed algorithm is evaluated by comparing tracking estimates with ground truth. We use precision and recall as metric to evaluate our tracking estimates with ground truth. Our vehicle counting result also evaluated by comparing to ground truth. Our tracking result have 96% recall in all experiment scenarios. Also, we have 1.3% and 7.4% error rate in our counting result compared to ground truth.
机译:印度尼西亚的车辆增长不受道路数量的支持。这一事实造成了很容易发生的交通拥堵,特别是在大城市。智能交通系统(其)是此问题的解决方案之一。在其车辆跟踪和计数中,对于交通监测来说是一个具有挑战性的问题。本文提出了基于BHATTACHARYYA距离的跟踪和车辆计数。我们提出了一种车辆跟踪方法和车辆计数系统。我们使用具有孔填充算法的自适应背景学习,将背景和前景分开在框架中。我们使用每个对象的轮廓检测前景区域中的对象。每个检测到的对象都使用Bhattacharyya距离跟踪。当跟踪对象传递预定义的计数行时,计算每个跟踪对象。通过将跟踪估计与地面真理进行比较来评估所提出的算法。我们使用精度并召回为度量标准,以评估与地面真理的跟踪估计。我们的车辆计数结果也通过与地面真理进行评估。我们的跟踪结果在所有实验方案中都有96 \%召回。此外,与地面真理相比,我们在计数结果中有1.3 \%和7.4 \%错误率。

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