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Illumination invariant method to detect and track left luggage in public areas

Surveillance and its security applications have been critical subjects recently with various studies placing a high demand on robust computer vision solutions that can work effectively and efficiently in complex environments without human intervention. In this paper, an efficient illumination invariant template generation and tracking method to identify and track abandoned objects (bags) in public areas is described. Intensity and chromaticity distortion parameters are initially used to generate a binary mask containing all the moving objects in the scene. The binary blobs in the mask are tracked, and those found static through the use of a 'centroid-range' method are segregated. A Laplacian of Gaussian (LoG) filter is then applied to the parts of the current frame and the average background frame, encompassed by the static blobs, to pick up the high frequency components. The total energy is calculated for both the frames, current and background, covered by the detected edge map to ensure that illumination change has not resulted in false segmentation. Finally, the resultant edge-map is registered and tracked through the use of a correlation based matching process. The algorithm has been successfully tested on the iLIDs dataset, results being presented in this paper.