利用鱼眼相机对人群进行运动估计

人群运动估计是人群行为分析的重要步骤。特定场景的人群运动分析和监控，是维护公共安全和社会稳定的一个必要措施，也是视频监控领域的一个研究难点。利用鱼眼相机视场大、无视觉盲区的优点，提出了一种基于特征点光流的人群运动估计方法。首先，采用一种基于面积反馈机制的混合高斯背景差分方法，对原始视频图像进行预处理，并利用圆拟合的方法获取兴趣区域；其次，为了在保证准确描述人群目标的同时提高算法的实时性，提出一种基于边缘密度非均匀采样的人群特征点提取方法来描述运动的人群目标，并利用Lucas & Kanade光流法计算光流场；最后，为了解决远近人群的尺寸大小不一致的问题和鱼眼相机的畸变问题，采用鱼眼相机的透视加权模型，计算人群运动加权统计直方图，获取人群在鱼眼图像中的全局运动方向和速度。实验结果表明，针对密集的人群，该方法能有效、实时地估计人群的运动方向和速度，为人群行为分析提供有力的研究基础。

Crowd Motion Estimation Using a Fisheye Camera

Crowd motion estimation is an important part of crowd action analysis. Crowd motion Analysis in special places is a necessary action for maintaining the safety and social stability in public place and there is a research difficulty in the field of intelligent video monitoring. Existing approaches for crowd motion estimation based on traditional cameras have the limitation of small field-of-view and more blind spots. This paper proposes a crowd motion estimation approach based on the feature point optical flow employing the advantages of large field-of-view and no blind spot of fisheye cameras. Firstly, the original images are preprocessed using the method of background difference based on Gaussian Mixture Model with area feedback, and the region of interest (ROI) is obtained by circle fitting. Secondly, a feature point extraction method based on non-uniform sampling of edge density is presented to describe the moving crowd for improving the real-time performance as the same time as ensuring the accuracy of describing the crowd. And then the optical flow field is calculated using the method by Lucas & Kanade. Finally, a perspective weight model of the fisheye camera is developed to weighting the compute the motion vector and the motion direction and speed of the crowd in fisheye camera images in order to solve the issues of the size differences of the crowd in long and short distances and the distortion of fisheye images in this paper. The experimental results show that the proposed approach is effective and feasible for estimating the motion speed and orientation of the crowd in dense crowd. In addition, the proposed method provides an important research basis for crowd behavior analysis.