Tracking illegally parked vehicles using correlation of multi-scale difference of Gaussian filtered patches

Detection and tracking of illegally parked vehicles are usually considered as crucial steps in the development of a video-surveillance based traffic-management system. The major challenge in this task lies in making the tracking phase illumination-change tolerant. The paper presents a two-stage process to detect vehicles parked illegally and monitor these in subsequent frames. Chromaticity and brightness distortion estimates are used in the first stage to segment the foreground objects from the remainder of the scene. The process then locks onto all stationary 'vehicle'-size patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the illegally parked vehicles detected during the first stage. All the locked patches are filtered using a difference-of-Gaussian (DoG) filter operated at three different scales to capture a broad range of information. In succeeding frames patches at the same locations are similarly DoG filtered at the three different scales and the results matched with the corresponding ones initially generated. A combined score based on correlation estimates is used to track and confirm the existence of the illegally parked vehicles. Use of the DoG filter helps in extracting edge based features of the patches thus making the tracking process broadly illumination-invariant. The two-stage approach has been tested on the United Kingdom Home Office iLIDS dataset with encouraging results