Occlusion and lack of visibility even in sparse crowd scenes make it difficult to track individual pedestrians correctly and consistently, particularly in a single view. We present a novel pedestrian tracking approach that connects tracking with reidentification to locate and maintain the identity of certain people who may be occluded for a long time. First, two models are constructed. One model tracks the pedestrian and trains a classifier, while the other model reidentifies the pedestrian of interest from detection results with the trained classifier. Secondly, we design a set of transition rules for model switching. Finally, the two models work alternatively based on the principle of a hybrid system to track the pedestrian. Several typical sets of experiments show that the proposed approach outperforms the state-of-the-art approaches and achieves robust pedestrian tracking in the presence of full occlusion.