Tracking by local structural manifold learning in a new SSIR particle filter

Abstract

We propose a new object tracking algorithm by local structural manifold learning in a selective sampling importance resampling (SSIR) particle filter framework. A new local structural manifold learning strategy is designed for the invariant appearance modeling in challenging conditions. The appearance of the object which has complex structure in the low-dimensional space is approximated with a set of local structural manifolds. The local structures of the appearance manifold are incrementally learned with changes in the appearance of the object. Unlike traditional particle filters which rely on random re-sampling for new particles generation, we propose a new SSIR particle filter, which integrates an auto-regressive filter to improve the process of samples generation. The distribution of the generated particle samples by our method is better than that of the traditional techniques. Experimental results on several challenging videos demonstrate the robustness and accuracy of our algorithm compared with other recent excellent tracking approaches.