Predictive Saliency Maps for Surveillance Videos

When viewing video sequences, the human visual system (HVS) tends to focus on the active objects. These are perceived as the most salient regions in the scene. Additionally, human observers tend to predict the future positions of moving objects in a dynamic scene and to direct their gaze to these positions. In this paper we propose a saliency detection model that accounts for the motion in the sequence and predicts the positions of the salient objects in future frames. This is a novel technique for attention models that we call Predictive Saliency Map (PSM). PSM improves the consistency of the estimated saliency maps for video sequences. PSM uses both static information provided by static saliency maps (SSM) and motion vectors to predict future salient regions in the next frame. In this paper we focus only on surveillance videos therefore, in addition to low-level features such as intensity, color and orientation we consider high-level features such as faces as salient regions that attract naturally viewers attention. Saliency maps computed based on these static features are combined with motion saliency maps to account for saliency created by the activity in the scene. The predicted saliency map is computed using previous saliency maps and motion information. The PSMs are compared with the experimentally obtained gaze maps and saliency maps obtained using approaches from the literature. The experimental results show that our enhanced model yields higher ability to predict eye fixations in surveillance videos.