Moving Vehicle Detection Based on Optical Flow Method and Shadow Removal
Moving Vehicle Detection Based on Optical Flow Method and Shadow Removal
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DOI：
10.1007/978-3-030-48513-9_36
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Min Sun;Wei Sun;Xiaorui Zhang;Zheng Zhu;Mian Li
中科院分区：
文献类型：
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作者：
Min Sun;Wei Sun;Xiaorui Zhang;Zheng Zhu;Mian Li
基于视频的运动车辆检测是车辆跟踪和车辆计数的重要前提。然而，在自然场景中，由于阴影的产生，传统的光流法不能准确地检测出运动车辆的边界。针对这一问题，提出了一种基于光流法和阴影去除的运动车辆检测改进算法。该方法首先利用光流法对运动车辆进行粗略检测，然后利用基于HSV颜色空间的阴影检测算法对阈值分割后的阴影位置进行标记，并进一步结合区域标记算法实现阴影去除，准确检测出运动车辆。在有阴影干扰的复杂交通场景中进行了实验。实验结果表明，该方法能很好地解决阴影干扰对运动车辆检测的影响，实现运动车辆的实时准确检测。
Video-based moving vehicle detection is an important prerequisite for vehicle tracking and vehicle counting. However, in the natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In order to solve this problem, this paper proposes an improved moving vehicle detection algorithm based on optical flow method and shadow removal. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle, and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation, and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Experiments are carried out in complex traffic scenes with shadow interference. The experimental results show that the proposed method can well solve the impact of shadow interference on moving vehicle detection and realize real-time and accurate detection of moving vehicles.