Mean shift object tracking with modified LTP
Shaona Zhang, Dong Hu, Yanting Hu · 2012
A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .