基于前—后向光流点匹配运动熵的视频抖动检测算法

目前大部分视频监控系统面临着高效实时性智能分析与低效滞后的人工故障排查的矛盾。视频质量智能诊断系统可以为此提供有效的解决方案。针对视频质量诊断系统中的画面抖动异常检测问题，提出一种简单有效的实用算法。该算法通过有效融合图像的稀疏光流与特征点匹配算法，根据前向-后向误差标准估计图像帧的全局运动参数，引入连续帧的运动熵用于衡量视频画面片段运动的混乱程度，判断是否存在视频抖动现象。算法在不同分辨率的实际监控录像数据集上进行了测试和比较。实验证明，该算法在一定程度上克服了大位移抖动的影响，具备良好的实时特性以及较高的检测精度，能够满足实际工作的需求。

Video jitter detection algorithm based on forward-backward optical flow point matching motion entropy

The conflicts between the real-time, efficient intelligent analysis and the inefficient, laborious trouble shooting, which are faced by most of video surveillance systems, can be resolved by Intelligent Video Quality Detection System (IVQDS). As a part of IVQDS, video jitter detection algorithm was focused in this paper. In the proposed method, sparse optical flow features were fused together with interest point matching algorithm; correctly matched point-set which was reliably detected according to the forward-backward error criterion, was used to estimate the global motion parameters, from which motion entropy was computed to measure the motion homogeneity of the video fragment. The experimental results tested on realistic surveillance video records have shown that the proposed algorithm can work under real-time environment against the effects from big movements with high detection performance.