An Algorithm of Object Tracking Based on Kernel Density Correlation
基于核密度相关度量的视频目标跟踪

In the 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 object through looking for the regional minimum of the distance function iteratively.But there exist some limits because of the loss of space distribution.To overcome this problem,the method based on correlation between kernel density estimation of tracking regions is proposed. Furthermore, some experiments show that it can improve accuracy and robustness of this tracking algorithm.