Automatic Objects Detection and Tracking Using FPCP, Blob Analysis and Kalman Filter


Object detection and tracking are key mission in computer visibilityapplications, including civil or military surveillance systems. However,there are major challenges that have an effective role in the accuracy ofdetection and tracking such as the ability of the system to track the targetand the response speed of the system in different environments as well asthe presence of noise in the captured video sequence. In this proposedwork, a new algorithm to detect moving objects from video data isdesigned by the Fast Principle Component Purist (FPCP). Then, we usedan ideal filter that performs well to reduce noise through themorphological filter. The Blob analysis is used to add smoothness to thespatial identification of objects and their areas, and finally, the detectedobject is tracked by Kalman Filter. The applied examples demonstratedthe efficiency and capability of the proposed system for noise removal,detection accuracy and tracking.