基于行为一致性的密集场景人群分群检测算法

针对公共场所无序运动人群密集场景,提出了一种无监督的自动人群分群检测算法。在由高斯混合模型背景去除法得到的前景区域提取KLT特征点,通过分析特征点的运动特性,对邻域特征点采用速度方向过滤算子和运动相关性过滤算子进行逐级过滤;对非邻域特征点采用运动轨迹相似性过滤算子进行过滤,遍历所有特征点,以实现人群的分群检测。该算法不需要对单个行人进行分割及样本训练,也不需要任何先验信息,且在邻域特征点划分过程中,邻域的特征点数能根据相邻距离特征点的最短距离进行自动调节。采用具有不同运动模式的密集场景视频对所提出的算法进行了试验,结果验证了算法的有效性、可靠性和优越性。

A Group Detecting Algorithm Based on Behavior Consistency in Crowd

Efficient complete intelligent video surveillance system has become the urgent needs in today''s society.For the crowded scenes involving multiple complex motion modalities,an unsupervised automatic detection group algorithm in the crowd is proposed,which firstly extracts Kanade-Lucas-Toma (KLT) points from the foreground fields based on Gaussian Mixture Models (GMMs) background subtraction strategy.Then for each foreground-keypoint,velocity direction filtering operator,motion correlations filter operator is applied to move behavior inconsistent neighboring points,and non-neighbors are filtered based on path-based similarity.Thus,all foreground-keypoints are merged and split each other to achieve group detection.This method does not need single pedestrian segmentation,training sample and prior information;meanwhile,the number of neighbors is self-tuning according to the nearest distance neighboring keypoints in foreground fields.To test the performance of this algorithm,videos with multiple complex motion modalities are used in experiments.Experimental results show that the proposed algorithm is more efficient and more robust in complex crowded motion scenes.