Adaptive Multiple Appearances Model Framework for Long-Term Robust Tracking

Abstract

Tracking an object in long term is still a great challenge in computer vision. Appearance modeling is one of keys to build a good tracker. Much research attention focuses on building an appearance model by employing special features and learning method, especially online learning. However, one model is not enough to describe all historical appearances of the tracking target during a long term tracking task because of view port exchanging, illuminance varying, camera switching, etc. We propose the Adaptive Multiple Appearance Model (AMAM) framework to maintain not one model but appearance model set to solve this problem. Different appearance representations of the tracking target could be employed and grouped unsupervised and modeled by Dirichlet Process Mixture Model (DPMM) automatically. And tracking result can be selected from candidate targets predicted by trackers based on those appearance models by voting and confidence map. Experimental results on multiple public datasets demonstrate the better performance compared with state-of-the-art methods.