Tracking Non-rigid Object Using Discriminative Features
Qian Wang, Qingxuan Shi, Xuedong Tian · 2014
We propose a simple but effective tracking algorithm for non-rigid objects with geometric appearance changes. The discriminative features of the object are adaptively selected according to their descriptive ability. To adapt to the geometric changes, we use a deformable rectangle to represent the object, and use Markov Chain Monte Carlo-based Particle Filter (MCMC-PF) to estimate the state of the object in a restricted four dimensional space. Experimental results show that the proposed tracking algorithm has ideal performance.