Neural Background Subtraction for Pan-Tilt-Zoom Cameras

We propose an extension of a neural-based background subtraction approach to moving object detection to the case of image sequences taken from pan-tilt-zoom (PTZ) cameras. The background model automatically adapts in a self-organizing way to changes in the scene background. Background variations arising in a usual stationary camera setting, such as those due to gradual illumination changes, to waving trees, or to shadows cast by moving objects, are accurately handled by the neural self-organizing background model originally proposed for this type of setting. Handling of variations due to the PTZ camera movement is ensured by a novel registration mechanism that allows the neural background model to automatically compensate the eventual ego-motion, estimated at each time instant. Experimental results on several real image sequences and comparisons with seven state-of-the-art methods demonstrate the accuracy of the proposed approach.