In the non-overlapping multi-camera or single camera video surveillance, re-identification of tracked target is very important. Due to weakness of traditional support vector machine in feature fusion, a new people re-identification method is proposed based on online multiple kernel learning. We extract complementary visual word tree histogram and global color histogram from tracked people foreground image sequence in video, and then multiple kernel learning method is used for online train people visual appearance. Finally, we obtain multiple kernel feature fusion model of people appearance. Experimental results show that our method can train people appearance model rapidly, meet the real-time requirement of video surveillance, and attain higher recognition performance than single feature appearance model and single kernel support vector machine method.