Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Digitally modeling, visualizing and preserving archaeological sites
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Detection and Monitoring of Passengers on a Bus by Video Surveillance
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Graffiti Detection Using a Time-Of-Flight Camera
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Event Composition with Imperfect Information for Bus Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
MetroSurv: detecting events in subway stations
Multimedia Tools and Applications
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Voting-based simultaneous tracking of multiple video objects
IEEE Transactions on Circuits and Systems for Video Technology
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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.