An approach for Object Detection and Object Tracking using Background Subtraction

Computer Science & Engineering

Usually, the video based object tracking deal with non-stationary image stream that change over time. Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. Robust and real-time moving object tracking is a problematic issue in computer vision research area. In the existing system researchers have designed a modified PN learning algorithm for enhancing the performance of system. Modified PN learning algorithm achieves performance to increase the frame processing by adding background subtraction technique for any real-time object detection to track single object in video stream. Special feature used in this technique is Region of Interest (ROI) which minimizes the searching region. Existing system works poor for multiple moving object detection. The proposed system is designed for multiple moving objects tracking in real-time. In proposed system we use background image update, color segmentation and edge detection technique. We use border detection method for better object detection. There are problem in tracking real-time multiple object through a sequence of video. Thus, we solve this problem using Particle filter techniques that has been proven to be a robust algorithm to any real-time moving object.

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