With the maturity of object detection models, tracking-by-detection has become the mainstream of multi-object tracking research. Assisted by the almost perfect object detection results, data association can be formed only through the IoU information. However, in practice, a small number of missing detections will cause a large number of ID switches and fragmentations, which will seriously affect the tracking results. To solve this problem, the multiple object tracking algorithm is proposed with the introduction of image information. Specifically, preliminary tracking results obtained through the IoU model are verified with the pedestrian feature vector, and for the tracks that have not passed the verification, they are re-matched. For the problem of occlusion, the algorithm adopts the method of predicting the object trajectory and taking measures in advance. Tested on MOT16 and 2DMOT15 datasets, the algorithm has achieved good results, and it is more suitable for practical applications with its online tracking mode.