Real-time vandalism detection by monitoring object activities

Abstract

This paper proposes a novel method for the detection of vandalism events in video sequences. The method is based on a proposed definition for common vandal behaviors recorded on surveillance video sequences. To do this, the method monitors changes inside a restricted site containing vandalism-prone objects such as a vending machine, a pay phone, or a street sign. When an object is detected as leaving such a site, the proposed method checks if the site contains temporally consistent and significant static changes, representing damage. If there are such changes and given that the site is normally unchanged after legal use, a vandalism event is declared and the vandals are tracked. The proposed method is tested on video sequences showing real and simulated vandal behaviors and it achieves a detection rate of 96%. It detects different forms of vandalism such as graffiti and theft, and can handle sudden illumination changes, occlusions, and segmentation errors. The proposed method operates at a frame rate of 13 frames per second.