Computer Vision Techniques for Crowd Density and Motion Direction Analysis
Apeksha Chipade, Pallavi Bhagyawant, Pratiksha Khade, Rajshri C. Mahajan, Vibha Vyas · 2021
A system using computer vision techniques for tracking and providing early information of hazardous locations in huge gatherings is the need of the hour. Also, the number of video streams generated are huge and are challenging to watch them personally. The proposed system is based on the Optical Flow based estimations and detects sequences of crowd motions that are characteristic for devastating congestions. Initially, the temporal features of the scenes are extracted using Motion History Image (MHI) technique. MHI technique involves the weighted subtraction of consecutive frames of the video stream. Then the Optical Flow vectors are calculated using the Lucas-Kanade method. Segmentation of Optical Flow fields is done, and hence directional histograms of motion magnitude against motion direction are determined for respective segments. Their entropy and absolute bin magnitudes characterize the graphs. Thresholds are chosen such that there is a demarcation between sparsely crowded and densely crowded segments in the frames. Localization of crowd density levels as unpopulated, sparse and dense helps in providing immediate attention to critical areas of congestion. The directional analysis helps in having knowledge of the significant direction of motion in sparsely crowded regions.