Tracking illegally parked vehicles using correlation of multi-scale difference of Gaussian filtered patches
2011
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.
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