A scale rotation adaptive new mean shift tracking method
Heng Zhang, Lichun Li, You Li, Qifeng Yu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
The mean shift algorithm is an efficient technique for tracking 2D blobs through an image. The scale of the mean shift kernel is a crucial parameter. Classic Mean shift based tracking algorithm uses fixed kernel-bandwidth, which limits the performance when the object scale exceeds the size of the tracking window. Although some modified algorithms can settle the problem of object zooming in a way, these algorithms are helpless to the object rotation. Based on the analysis of the scale-space theory and the current Mean shift algorithms, a scale and rotation adaptive mean shift tracking algorithm is proposed. Experimental results show that the new method can effectively and accurately obtain the best description of the target areas for the first frame, and the new mean shift tracking algorithm can adapt to any kind of object's movements.