Object tracking using Harris corner points based optical flow propagation and Kalman filter

This paper proposes an objects tracking method using optical flow information and Kalman filtering. The basic idea of the proposed approach starts from the fact that interesting points based optical flow is more precise and robust when compared to the optical flow of the other pixels of objects. Firstly, objects to be tracked are detected basing on independent component analysis. For each detected object, Harris corner points are extracted and their local optical flow is calculated. The optical flow of the Harris points is then propagated using a Gaussian distribution based technique to estimate the optical flow of the remaining pixels. Finally, the estimated optical flow is corrected using an iterative Kalman Filter. Experimental results on real data set frames are presented to demonstrate the effectiveness and robustness of the method. This work is developed within the framework of the PANsafer project, supported by the ANR VTT program.