Optical Flow Approach for Real-Time Crowd Activity Identification Using Segmentation

Abstract

This research introduces an optical flow approach for real-time crowd activity identification using segmentation methods. The methodology focuses on utilizing the optical flow field to analyze motion patterns within crowd scenes, thereby segmenting the scene into coherent motion regions for activity identification. Through clustering of optical flow vectors, distinct motion regions are delineated, facilitating the identification of various crowd activities such as walking, running, and gathering.Efficient algorithms for optical flow computation and segmentation are implemented to ensure real-time performance. The research is validated through implementation and testing on diverse crowd scenes, demonstrating its efficacy in accurately identifying different crowd activities in real time. Experimental results showcase the superiority of the optical flow-based segmentation method over traditional techniques in terms of accuracy and computational efficiency, thus presenting a promising solution for real-time crowd activity identification systems.