19 March 2013 Recognition of two-person interaction in multi-view surveillance video via proxemics cues and spatio-temporal interest points
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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.
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Bo Zhang, Bo Zhang, Paolo Rota, Paolo Rota, Nicola Conci, 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); doi: 10.1117/12.2003686; https://doi.org/10.1117/12.2003686
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