Visual tracking via incremental self-tuning particle filtering on the affine group

Min Li, Wei Chen, Kaiqi Huang, Tieniu Tan

Abstract: We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group. SIFT (Scale Invariant Feature Transform) like descriptors are used as basic features, and IPCA (Incremental Principle Component Analysis) is utilized to learn an adaptive appearance subspace for similarity measurement. ISPF tries to find the optimal target position in a step-by-step way: particles are incrementally drawn and intelligently tuned to their best states by an online LWPR (Local Weighted Projection Regression) pose estimator; searching is terminated if the maximum similarity of all tuned particles satisfies a target similarity distribution (TSD) modeled online or the permitted maximum number of particles is reached. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of random particles.