The Research On Anti-occlusion And Anti-resampling Of Particle Filter Tracking Algorithm

The kalman filter, mean shift algorithm and the particle filter algorithm n have appeared through constant development of target tracking technology. Th e performance of the algorithm determines the tracking effect is good or bad. At present the mainstream of the tracking algorithm s is suit to relatively simple situations. However, for the more complicated situations, tracking accuracy will be reduced and failure of the tracking will be occured. And it can not meet the requirements of the tracking performance. Therefore, improving the performance of the tracking algorithm under complex environment is a hot research topic.For similar to the target color ba ckground environment, especially the shade, a lot of tracking erro rs, and even failures will appear in the particle filter target tracking algorithm. In complex environments, such as light, shade, and rotating particle filter tracking algorithm tracking ac curacy decline, even fail, and other issues.This paper have stud y how to improve the tracking algorithm to improve ability of anti-blocking and anti-resample and tracking accuracy on the basis of the traditional particle filter algorithm combin ing with color and texture in the case of illumination, occlusion and rotation. The main contents include :1. In a complex environment, the algorithms of single color, single texture, color and texture combined have been done track experiments. Different tracking results have been got by changing the different background environments.we can obtain the three algorithms have particle degradation after several iterations and not ability to deal with the occlusion by analyzing of three traditional tracking method for tracking trends and the accuracy of the algorithm to adapt to the degree of occlusion, light and rotation.2. For the problem of needing resample, likelihood distribution adaptivel y(Adaptive Likelihood Distribution, ALD) method have been proposed. Likelihood distribution has been adjusted according to the size of noise increasing the overlapping area and effectively improv ing the stability of the filter and reduced the numbers of resampl e. In different tracking environment, the number of samples have been reduced by d ynamic particle threshold based on the area occupied by the particles and the time complexity of the algorithm have been also reduced by using the d ynamic number of particles under ensurin g the accuracy of the premise of a certain track.3. For the failure of the tracking cased by serious occlusion, improved ternary mode(Local Ternary Patterns, LTP) with the higher tracking accuracy have been used as feature and use the occlusion processing algorithms to process deciding the tracking area using template update strategy to determine if it need to update the template, then continue to track. Experimental results show that the algorithm also have a good tracking accuracy in the case of occlusion and a good tracking robustness for many tracking environme nt.