In order to solve the problems of object appearance change caused by severe occlusion, non-rigid deformation and object departure from the field of view in multi-scale search strategy tracking of DSST algorithm, an algorithm was proposed to fuse the support vector machine (SVM) object re-detection module. Multiple features of the target are extracted and these feature vectors are fused to enhance the feature expression of the target. Based on the position filter and scale filter of DSST algorithm, the target appearance filter is added, the trained SVM is used to search for the target globally. Different window samples are used to train relevant models and establish an optimal classification surface of SVM. The missing target is re-detected by SVM classifier. The experimental results show that the improved algorithm has better robust performance than DSST algorithm on such problems as target occlusion and non-rigid deformation.