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Real Time Recognition and Monitoring of Moving Targets in Video using Kalman Filter

The video surveillance system supports the classification of objects and the tracking of objects. The tracking of objects is considered as one of the utmost important tasks of the video surveillance system. Two key steps in these systems are: detection and frame-tracking of the object. Object detection involves identifying an object in the frame of a video stream. Each tracking technique needs a mechanism to detect moving objects. The Background subtraction methodology and Frame difference methodology are most commonly uses target detection algorithms. This paper proposes an adaptive filter approach to keep track of a moving object in real time video. Kalman filter can be applied to linear systems. The Kalman filter is a robust algorithm that can be used in state estimation issues, so this method is used to estimate and evaluate the location of a moving target. In this paper we use the Kalman Filter approach to track the moving target. Performance of Kalman Filter is evaluated using parameters Percentage Fit Error and Root Sum Square Position Error. A MATLAB code is developed and the simulated result shows good tracking performance of real time video.