Visual Tracking via Online Nonnegative Matrix Factorization

Yi Wu, Bin Shen, Haibin Ling

Abstract: In visual tracking, holistic and part-based representations are both popular choices to model target appearance. The former is known for great efficiency and convenience, while the latter for robustness against local appearance or shape variations. Based on nonnegative matrix factorization (NMF), we propose a novel visual tracker that takes advantage of both groups. The idea is to model the target appearance by a nonnegative combination of nonnegative components learned from examples observed in previous frames. To adjust NMF to the tracking context, we include sparsity and smoothness constraints in addition to the nonnegativity one. Furthermore, an online iterative learning algorithm, together with a proof of convergence, is proposed for efficient model updating. Putting these ingredients together with a particle filter framework, the proposed tracker, constrained online nonnegative matrix factorization (CONMF), achieves robustness to challenging appearance variations and nontrivial deformations while running in real time. We evaluate the proposed tracker on various benchmark sequences containing targets undergoing large variations in scale, pose, or illumination. The robustness and efficiency of CONMF is validated in comparison with several state-of-the-art trackers.