基于图像熵的密集人群异常事件实时检测方法

摘要

In the field of intelligent video surveillance, in order to improve the efficiency of abnormal event detection and the defects of present methods in poor real-time performance and applicability, this paper proposes a real-time and high efficiency method. This method firstly extracts the global optical flow value as the movement characters, and con-structs the visualizing expression of global optical flow. Then the image entropy analysis is used to obtain the statisti-cal parameter in normal conditions. Finally, the confidence interval in normal condition and the anomaly judgment for-mula are given, which can be used to detect the abnormal event. The experimental results show that, for the video size of 320×240, the average detection time can be as low as 0.031 s in each frame and the accuracy can reach above 96%. As a result, the method has high efficiency and good real-time.%在智能视频监控领域，为了提高密集人群中异常事件的检测效率，改善已有算法在实时性和适用性方面的不足，提出了一种实时高效的检测方法。该方法首先提取图像的全局光流强度作为运动特征，并构造全局光流强度的图像化表达；然后利用图像熵进行分析，获取正常状态下图像熵的统计参数；最后确定正常状态的可信区间和自适应的异常判定公式，从而判断异常事件是否发生。实验结果表明，该算法对尺寸为320×240像素的视频，平均每帧的检测时间低至0.031 s，且准确率可达96%以上，具有较高的检测效率，且实时性较好。