Robust tracking based on pixel-wise spatial pyramid and biased fusion

Abstract

We propose a novel tracking algorithm for the balance between stability and adaptivity as well as a new online appearance model. Since the update error is inevitable, we present three tracking modules, i.e., reference model, soft reference model and adaptive model, and fuse them using biased multiplicative formula. These three contributors are built through the same appearance model with different update rate. The appearance model, Pixel-wise Spatial Pyramid, employs pixel feature vectors instead of SIFT vectors, to combine several pixel characteristics. In particular, the reserved pixel feature vectors are used to create a new codebook together with the earlier codebook. A hybrid feature map consisting of the reserved pixel vectors and anti-part of previous hybrid feature map is built to represent the new target map. Experimental results show that our approach tracks the object with drastic appearance change, accurately and robustly. © 2011 Springer-Verlag Berlin Heidelberg.