Visual object tracking is widely used in various fields such as video intelligent monitoring and visual navigation of robot. Aiming at the problem that traditional kernelized correlation filter (KCF) tracking method is lack of target scale estimation, and performs poor in video sequence whose target scale changes observably, a multi-scale estimation modified method is proposed. This method uses reference for discriminative spacial scale tracker (DSST) to make scale estimation by adopting the scale pyramid correlation filter. The gray image pyramid is mapped to a one dimensional feature vector, and then this vector is used as input of scale correlation filter. The target scale is estimated from the highest response values. This modified method is tested on benchmark data set, and it is compared with the other current visual tracking methods. The results verify the high efficiency of the proposed algorithm. The proposed algorithm has strong adaptability under complex conditions, such as object scale change, illumination variation, posture change, partial sheltering, rotating and rapid movement.