Video Surveillance Based Tracking System
The paper presents video surveillance-based tracking system for the outdoor environment. The video processing has been done using both LabVIEW and MATLAB, and the comparisons are illustrated. In LabVIEW, self-learning and real-time moving object tracking are implemented for the surveillance of environment. In a two-dimensional color pattern matching, a self-learned template has to be located on real-time video, regardless of the template’s position, color, and shape. It is done by organizing a set of feature vectors that encompass all the variations in the self-learned template. Matching is then done by determining the best similarity between the feature vectors extracted from the video image and the self-learned template set. Template learning time and elapsed time are taken as parameters for the comparison. In MATLAB, tracking is achieved using Camshift algorithm. The feature vectors include position, orientation, and size of the object to be tracked.
KeywordsCamshift algorithm Self-autonomous video surveillance Tracking system Region of interest LabVIEW and MATLAB
The authors wish to thank Department of Science and Technology for awarding a project under Cognitive Science Initiative Programme (DST File No.: SR/CSI/09/2011) through which the work has been implemented.
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