Special Section on Video Surveillance and Transportation Imaging Applications

ViCoMo: visual context modeling for scene understanding in video surveillance

[+] Author Affiliations
Ivo M. Creusen

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

Cyclomedia Technology B.V., Van Voordenpark 1, Zaltbommel, The Netherlands

Solmaz Javanbakhti

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

Marijn J. H. Loomans

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

ViNotion B.V., Horsten 1, Eindhoven, The Netherlands

Lykele B. Hazelhoff

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

Cyclomedia Technology B.V., Van Voordenpark 1, Zaltbommel, The Netherlands

Nadejda Roubtsova

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

University of Surrey, Guildford, United Kingdom

Svitlana Zinger

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

Peter H. N. de With

Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands

Cyclomedia Technology B.V., Van Voordenpark 1, Zaltbommel, The Netherlands

J. Electron. Imaging. 22(4), 041117 (Sep 24, 2013). doi:10.1117/1.JEI.22.4.041117
History: Received April 17, 2013; Revised July 30, 2013; Accepted August 20, 2013
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Abstract.  The use of contextual information can significantly aid scene understanding of surveillance video. Just detecting people and tracking them does not provide sufficient information to detect situations that require operator attention. We propose a proof-of-concept system that uses several sources of contextual information to improve scene understanding in surveillance video. The focus is on two scenarios that represent common video surveillance situations, parking lot surveillance and crowd monitoring. In the first scenario, a pan–tilt–zoom (PTZ) camera tracking system is developed for parking lot surveillance. Context is provided by the traffic sign recognition system to localize regular and handicapped parking spot signs as well as license plates. The PTZ algorithm has the ability to selectively detect and track persons based on scene context. In the second scenario, a group analysis algorithm is introduced to detect groups of people. Contextual information is provided by traffic sign recognition and region labeling algorithms and exploited for behavior understanding. In both scenarios, decision engines are used to interpret and classify the output of the subsystems and if necessary raise operator alerts. We show that using context information enables the automated analysis of complicated scenarios that were previously not possible using conventional moving object classification techniques.

© 2013 SPIE and IS&T

Citation

Ivo M. Creusen ; Solmaz Javanbakhti ; Marijn J. H. Loomans ; Lykele B. Hazelhoff ; Nadejda Roubtsova, et al.
"ViCoMo: visual context modeling for scene understanding in video surveillance", J. Electron. Imaging. 22(4), 041117 (Sep 24, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041117


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