Robust visual tracking algorithm based on peak characteristics to determine model updating
In order to solve the problem that the traditional model updating algorithm has poor robustness in the case of occlusion,illumination change and self-rotation in visual tracking,this paper proposes a robust visual tracking algorithm of using peak characteristics to selectively update the model.In this algorithm,the target posi-tion is first determined through particle filtering tracking,then the current model is used to conduct a local exhaus-tive search near the result location of the current frame tracking,and the detected peak distribution is also used to determine the numerical matrix of target confidence.Finally,the peak-to-sidelobe ratio threshold judgment meth-od is employed to decide whether or not to update the current model.Simulation results show that the proposed al-gorithm can effectively update the target model,and that compared with the contrast algorithm,it can achieve a better tracking effect on the whole in dealing with the situation of usual occlusion,illumination change,self-rota-tion,etc.in visual tracking.
visual trackingparticle filterpeak characteristicspeak-to-sidelobe ratiodegree of confidencerobust optimization
国家科技期刊平台
NSTL
万方数据
维普
