The research of video tracking is developing forward wide-range and long-time object tracking. Pedestrian re-identification is the key technology of wide-range and long-time pedestrian tracking, and is foundation of follow-up behavior analysis. A pedestrian re-identification algorithm is proposed based on non-sparse multiple kernel Support Vector Machine (SVM). Firstly, we extract multilayer SIFT feature and multilayer color histogram feature of tracked pedestrians video image sequence. Then, we online fuse multilayer SIFT feature and multilayer color histogram feature to obtain pedestrian appearance models using non-sparse multiple kernel SVM. Finally, we re-identify pedestrian objects using the stored pedestrian appearance models. The method can be applied to the same pedestrian tracking across cameras in the multiple cameras video surveillance and recognition of pedestrian recurrences in the single camera video surveillance. The experiment results show that our method can rapidly train pedestrian object appearance models and achieve very high recognition rate.