Abstract: In order to improve the reliability of video target tracking under different complex environments,this paper proposes a target tracking fusion algorithm based on multi-time-space perception correlation filters,which combines the time and space characteristics of target and background.The time characteristic is established by calculating the consistency of changes between target frames and filter frames based on the correlation filter.The target spatial information is extracted from the mask matrix and the neighborhood of the target.Finally,in the objective function,the constraints term of time-space perception is introduced to enhance the learning ability of correlation filter to improve the robustness of interfering information.In order to improve the ability of adapting to a complex and changeable environment,time-space perception correlation filters are established respectively in the color space and orientation gradient space.Then an adaptive fusion mechanism of the two tracking results is established to accurately calculate the target position and scale,which effectively improves the generalization ability of the algorithm for different complex environments.To validate the effectiveness of the proposed algorithm,comparison experiments with other 11 algorithms were performed on the OTB standard data set.The experimental results show that the algorithm in this paper has good robustness for occlusion,deformation,illumination variation,fast motion and other disturbances in a variety of complex environments,and can effectively track the target.