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

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. © 2013 SPIE-IS&T.