Real-Time Object Tracking based on a Limited, Discontinuous Feature Set

N. Al-Najdawi, E.A. Edirisinghe, and H.E. Bez (UK)

Keywords

: Object Tracking, KalmanFilter, Features selection, KLT.

Abstract

We present a low cost automatic object tracking algorithm suitable for use in real-time video based security systems that have limited computational capabilities. The novelty of the proposed system is that it uses a simplified Kanade Lucas-Tomasi (KLT) technique to detect features of both continuous and discontinuous nature. As discontinuous feature selection is subject to noise, and would result in non-optimal feature based object tracking, we use a Kalman filter to seek for optimal estimates in tracking. We provide experimental results to demonstrate that the system is capable of accurately tracking objects in real time applications where scenes are subject to noise particularly resulting from occlusions and sudden illumination variations.

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