基于区域相似信息的自适应运动目标检测算法

当视频监控中存在动态背景干扰、鬼影现象和静止目标时,ViBe算法的检测性能较差。针对该问题,提出一种改进的ViBe算法。通过Otsu算法得到图像的动态阈值,提高算法在动态背景中的抗干扰能力,同时结合区域相似度判断鬼影、拖影或静止目标区域,自适应地对不同类别区域像素进行更新抑制。实验结果表明,改进算法在动态背景中表现出良好的鲁棒性,能够有效抑制鬼影以及静止目标产生的拖影,在保证实时性的前提下较原算法检测精度和综合评价指标F值分别提高0.309和0.2,具有更好的检测效果和检测性能。

Adaptive Moving Target Detection Algorithm Based on Regional Similarity Information

The detection performance of ViBe algorithm is poor when there are dynamic background interference,ghost regions and stationary targets in video monitoring.To address the problem,this paper proposes an improved ViBe algorithm.First,the Otsu algorithm is used to obtain the dynamic threshold value of images and enhance the ability of resisting dynamic background interference.At the same time,region similarity is used to judge ghost regions,smears,and stationary target area.It can also adaptively suppress the pixel update of different kinds of regions.Experimental results show that the improved ViBe algorithm has good robustness in the dynamic background,and can effectively suppress ghost regions and smearsgenerated by stationary targets.The proposed algorithm improves the detection accuracy by 0.309 while keeping real-time performance,and increases the overall evaluation index F value by 0.2,which means it has better detection performance.