Real-time object tracking based on a limited, discontinuous feature set

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.