Public Place Crowd Transaction Monitoring System

Abstract

Currently, the phenomenon of abnormal movement in public spaces by groups is becoming increasingly prominent, leading to issues concerning public flow and safety. The escalating problems of high crowd density, the presence of controlled dangerous items, and unexpected group activities highlight the necessity for timely detection in public settings. Timely identification of such scenarios will facilitate prompt responses and assistance from relevant government departments. Exploring how artificial intelligence technology can aid urban management personnel in effectively detecting abnormal group behaviors is crucial. Having the ability to swiftly and efficiently evacuate crowds in emergency situations holds significant practical importance. This paper employs deep learning methodologies to assist urban management personnel in efficiently monitoring crowd density and detecting abnormal behaviors. The aim is to maintain crowd density within reasonable limits and enable rapid and effective crowd evacuation in emergency situations. Detection of abnormal group behaviors typically involves methods based on global features, extracting feature patterns like optical flow from entire video segments and constructing corresponding histograms. Given that automatic classification of crowd patterns involves sudden and abnormal changes, a novel method is proposed to extract motion "textures" from dynamic STV (Space-Time Volume) blocks formed from real-time video streams.