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Aiming at the problems of strong coupling between the target and the background and the change of the apparent features caused by the target deformation in infrared target tracking, an algorithm based on multi-modal background model and Hough forest was proposed. Both the target and background feature information were integrated into the tracking framework, and Hough forest was utilized for training and updating the target component characterization information. The multi-modal background model was built upon the feature descriptions and the pattern lists. Along with the matching background pattern lists recording in the observed image sequence, the modal parameters were updated according to the matching results. After obtaining the regional information from the background modeling, the foreground and the background were respectively taken as the positive and negative sample sets for the training in the decision trees. The decision of target’s position was carried out through Hough voting operation according to the image blocks’ information stored by the leaf nodes. The images’ feature space was mapped to the Hough parameter space to generate the probability distribution map of the target, and the high confidence area was determined as the tracking position. Experimental results on the public infrared datasets show that the proposed algorithm can keep track the target stably under the circumstance that background clutter and target deformation interfere with the tracking process.