Recognition of two-person interaction in multi-view surveillance video via proxemics cues and spatio-temporal interest points

Bo Zhang; Paolo Rota; Nicola Conci

doi:10.1117/12.2003686

19 March 2013 Recognition of two-person interaction in multi-view surveillance video via proxemics cues and spatio-temporal interest points

Bo Zhang, Paolo Rota, Nicola Conci

Author Affiliations +

Proceedings Volume 8663, Video Surveillance and Transportation Imaging Applications; 866305 (2013) https://doi.org/10.1117/12.2003686
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

In this paper we propose a novel method to recognize different types of two-person interactions through multi-view surveillance cameras. From the bird-eye view, proxemics cues are exploited to segment the duration of the interaction, while from the lateral view the corresponding interaction intervals are extracted. The classification is achieved by applying a visual bag-of-words approach, which is used to train a liner multi-class SVM classifier. We test our method on the UNITN social interaction dataset. Experimental results show that using the temporal segmentation can improve the classification performance.

Citation Download Citation

Bo Zhang, Paolo Rota, and Nicola Conci "Recognition of two-person interaction in multi-view surveillance video via proxemics cues and spatio-temporal interest points", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 866305 (19 March 2013); https://doi.org/10.1117/12.2003686

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