A tracking algorithm of improved spatio-temporal context with Kalman filter

For the rapid target suffering from severe occlusion, the tracking accuracy of spatio-temporal context algorithm decreases. A novel tracking algorithm of improved spatio-temporal context with Kalman filter is proposed in the paper. The rectangular region of the tracking object is manually marked at the first frame, and the improved spatio-temporal context algorithm is then applied to track the target. The Euclidean distance of the image intensity in two consecutive frames determines the state of the target in the tracking process. We apply Kalman filter to reduce the influence of noise and predict and estimate the possible position of the target under severe occlusion, and obtain better rectangular region of the tracking object. The experimental results show that the algorithm of improved spatio-temporal context with Kalman filter can be used for high speed and highly maneuvering tracking target with different light intensities, and is robust for the target with varied scale and severe occlusion. Time consumption per frame is 34.07 ms. Geometric center error per frame is 5.43 pixel, 70.2% less than that via the spatio-temporal context algorithm. The contour area per frame is 13.08%, 52.7% less than that via the spatio-temporal context algorithm.