| Automatic Technology |
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| Detection of crowd state mutation based onbackground difference algorithm and optical flow algorithm |
| GAO Peng-hui, ZHAO Wu-feng, SHEN Ji-zhong |
| College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract A detection algorithm based on background difference algorithm and optical flow algorithm was proposed in terms of the motion characteristics of crowd state mutation events. The background difference algorithm was used to extract the foreground and find the feature points. Then the optical flow algorithm was employed to predict the positions of the feature points. The feature points were divided into several parts based on the direction of the light flow, and the cumulative acceleration was calculated to determine whether group abnormal events have occurred or not. The proposed method makes up for the deficiencies of low detection accuracy using background difference algorithm and low detection efficiency using optical flow algorithm separately. The accuracy and stability of the detection was enhanced to a great extent by dividing the feature points by the light flow direction. The experimental results show that the algorithm has high accuracy in the detection of crowd state mutation events and can meet the requirements of real-time. The algorithm has a great improvement in comprehensive performance compared with other similar detection algorithms.
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Received: 17 January 2017
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结合背景差分与光流法的人群状态突变检测
针对群体性异常事件中人群状态突变场景发生时的运动特征，提出结合背景差分和光流法的检测算法.对图像背景差分提取前景寻找特征点，利用光流法预测特征点位置，将特征点以光流运动方向为依据划分后处理数据，得到累积加速度进行判断.该算法弥补了单独使用背景差分算法检测准确率低和单独使用光流法检测效率低的缺陷，通过将特征点以光流运动方向划分处理数据，大幅度提高了检测的准确性和稳定性.经过实验测试，结果表明，该算法在人群状态突变异常事件检测中有较高的准确性，能够满足实时性要求，较同类检测算法在综合性能上有显著提高.
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