Nearest neighbor field driven stochastic sampling for abrupt motion tracking

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, the existing methods tend to explore the whole state space uniformly with an inefficiency preliminary sampling phase. In this paper, we propose a nearest neighbor field(NNF) driven stochastic sampling framework for abrupt motion tracking in which NNF provides us promising regions the target may exist, and thus can help to explore the state space more effectively. Our approach firstly computes NNF to determine the promising regions; subsequently, we adopt Smoothing Stochastic Approximate Monte Carlo(SSAMC) sampling scheme to accurately localize the target. SSAMC is robust to handle the noises in NNF by propagating a sample's information to its neighboring regions. Finally, we refine the result with sparse representation based template matching technique. The experimental results on challenging sequences show that our tracker outperforms other related methods by better accuracy and higher robustness.