Surveillance system based on spatio-temporal information

We need a reliable moving object detection method that can work under various disturbances, such as changes of lighting and movements of background objects, to realize practical automatic surveillance systems. Using local information alone could not achieve this goal. Hence, we propose a method exploiting information over space and time. Optical flow is first computed, and then regions with uniform flow are extracted. The regions are hypothesized as parts of detection targets. Assuming that the targets pass in the scene at constant speeds, we predict the path of each region, voting it in the space-time cube as in the Hough transform. If enough number of votes support a particular path, the method reports the detection of a moving object. Experimental results show that it can detect walking people in complex scenes with moving background objects, such as trees shaking in the wind