Kernel-Based Particle Filter for Target Tracking with Adaptive Multiple Features Fusion

The particle filtering has been extensively used for visual tracking due to its flexibility.However the conventional particle filtering and its improved variants usually diverge when the measurement model is not accurate enough.To address this problem,a kernel-based particle filter algorithm is proposed.The algorithm reweighs the particles by weights which are produced by kernel function with the distance between target state and particles,and the particles are resampled according to the resultant weights.With the above improved particle filter algorithm,an adaptive multiple features fusion target tracking method is proposed.The proposed tracking method dynamically assesses the discriminability of each feature with respect to foreground to background separability and adaptively computes the feature's fusion weight by some similarity measure.Experimental results show that the proposed tracking method is superior over the conventional particle filter based tracking methods.