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

Abstract

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