Real-time tracking algorithm based on improved Mean Shift and Kalman filter

In traditional Mean Shift algorithm, color histogram is usually used as the features vectors and the dissimilarity between the referenced targets and the target candidates is expressed by the metric derived from the Bhattacharyya coefficients. The traditional Mean Shift procedure is used to find the real position of the target by looking for the regional minimum of the distance function iteratively. While the target´s color is similar to the background, the algorithm will miss the target. This paper presents a new mean shift algorithm based on spatial edge orientation histograms, using space distribution and texture information as matching information. Meanwhile a Kalman filter will be used to predict the target´s position. Experimental results demonstrate that the proposed algorithm can deal with intricate conditions, such as significant clutter, partial occlusions, and it can track objects efficiently and robustly.