Multi-feature stationary foreground detection for crowded video-surveillance

We propose a novel approach for stationary foreground detection in crowds based on the spatio-temporal evolution of multiple features. A generic framework is presented to detect stationarity where history images model the spatio-temporal feature patterns. A feature is proposed based on structural information over each pixel neighborhood for dealing with shadows and illumination changes. A multifeature detector is composed by combining the history images of three features (namely, foreground, motion and structural information) to estimate the foreground stationarity over time, which is later thresholded to detect stationary regions. Experimental results over challenging video-surveillance sequences show the improvement of the proposed approach against related work as structural information reduces false detections, which are common in crowded places.