In computer vision tracking

the traditional model updating has poor robustness in solving the problem of occlusion

illumination change and self rotation. To improve these problems

this study proposes a new visual object tracking method. The algorithm firstly builds a candidate update sub-model library. Secondly

it determines the position and information of the current target by fusing the three complementary features of the tracking based on particle Filter. Finally

the algorithm divides the three characteristic histogram of the target and the candidate model library to calculate the similarity of the reliability weights

then determines whether the candidate sub-model library and current sub-model can be updated. Results show that the algorithm can effectively select to update the sub-model. Rather than the contrast algorithms

our method can achieve a better tracking accuracy to deal with the situation of occlusion

illumination change and self rotation. The proposed method updates the target model effectively and keeps the good robustness under various tracking scenarios.