Fusion of Structural Information in Object Tracking Using Particle Filter and Mean Shift

An inherent problem with particle filter is the so-called particle degeneracy,i.e.,how to balance the number of the particles and real-time tracking is a critical issue to consider.This paper presents an improved tracking algorithm by combining particle filter and mean shift.In our algorithm,only four particles are used and each one of them can converge to a local maximum.The final target position is a combination of weighted particles.The computational complexity decreased sharply in comparison with the conventional particle filtering.In addition,structural information is also employed to compensate for the insufficiency of pure color information.Because each particle can always converge to a local maximum,the particle degeneracy can rarely happen,as a result,the resampling and particle update steps are not used in our implementation.Experimental results show that our algorithm can robustly track the target even in cases where the color of tracked target is similar to that of the background,and it can also meet the demand of real-time tracking.