基于规律性变化背景的异常检测算法研究

Research on Anomaly Detection Algorithm Based on Regular Change Background

煤层气开采安全问题日益受到广泛关注,为解决包含抽水机往复运动的煤层气开采现场的视频监控异常检测问题,提出一种基于有规律变化背景的异常检测算法。该方法先采用基于三帧差法的区域分割算法将图像分割为静态背景区与包含抽水机往复运动的动态背景区。在分割静态背景区域与动态背景区域的同时,采用基于三帧差的Surendra算法提取静态背景区信息,建立背景模型。然后针对不同的背景区采用不同的异常检测算法,可以较好的排除正常的抽水机往复运动对场景中异常检测的干扰。对于静态背景区采用三帧差法与背景减除算法融合分割前景;对于动态背景区则采用三帧差法分割前景。实验表明该算法在煤层气开采场景中能准确检测到前景,并满足视频监控的实时性要求。

The mining safety problem of coal bed methane is becoming more and more widely concerned. In order to solve the problem of video monitoring anomaly detection in the coal bed gas mining field, which contains the reciprocating motion of the pump, a new anomaly detection algorithm based on the regular variation background is proposed. In this method, the segmentation algorithm based on the three-frame-difference method is used to divide the image into the static background and the dynamic background of the reciprocating movement of the pump. At the same time, in order to set up the background model, the Surendra algorithm based on the three frame difference is used to extract the static background area information. Then different abnormal detection algorithms are used in different background areas, which can be better to eliminate the normal pump reciprocating movement interference of anomaly detection in the scene. In the static background, the three frame difference method and the background subtraction algorithm are used to divide the foreground. For the dynamic background, the three frame differential method is used to divide the foreground. Experiments show that this algorithm can accurately detect the foreground in coal-bed methane scenes, and meet the requirement of real-time video monitoring.