Detecting Abnormal Massive Crowd Flows: Characterizing Fleeing En Masse by Analyzing the Acceleration of Object Vectors

Abstract

Surveillance systems play an important role in mitigating various types of misconduct. However, observing a vast number of surveillance feeds is an arduous task that can be solved with a computerized system. A motion-based estimation method is proposed to measure the weighted temporal difference between consecutive frames by using spatial mean-sigma observations. It calculates the temporal frame differences that indicate the object vectors (OVs), of which statistical characteristics are evaluated periodically to identify any crowd abnormality. The proposed method is able to accurately detect the unusual event at the earliest scene, based on original and public data sets.