Criminal Investigation Oriented Saliency Detection for Surveillance Videos

Detecting the salient regions, namely locating the key regions that contain rich clues, is of great significance for better mining and analyzing the crucial information in surveillance videos. Yet, to date, the existed saliency detection methods are mainly designed to fit human perception. Nevertheless, what we value most during in surveillance videos, i.e. criminal investigation attentive objects (CIAOs) such as pedestrians, human faces, vehicles and license plates, is often different from those sensitive to human vision in general situations. In this paper, we proposed criminal investigation oriented saliency detection method for surveillance videos. A criminal investigation attentive model (CIAM) is constructed to score the occurrence probabilities of CIAOs in spatial domain and novelly utilize score to represent saliency, thus making CIAO regions more salient than non-CIAO regions. In addition, we refine the spatial domain saliency map with the motion information in temporal domain to obtain the spatio-temporal saliency map that has high distinctiveness for regions of moving CIAOs, static CIAOs, moving non-CIAOs and static non-CIAOs. Experimental results on surveillance video datasets demonstrate that the proposed method outperforms the state-of-art saliency detection methods.