Discriminative Bag-of-Words-Based Adaptive Appearance Model for Robust Visual Tracking

In this letter, we propose a novel discriminative bag-of-words (DBoW) model that can both adapt to appearance variations over time and reduce the commonly observed drifting problem in online tracking. Specifically, a contextual region containing both the object and its surroundings is explored to construct a compact representation with two bags-of-words. Each visual word is learned to carry discriminative appearance cues for the object. In order to alleviate the drifting problem, an adaptive updating approach is introduced to prevent the integration of the background into the object model. Based on DBoW model, a robust and near real-time tracker is proposed, where tracking is accomplished by searching the candidate that best matches to the maintained DBoW model. Extensive experimental results demonstrate competitive performance of the proposed method to state-of-the-art algorithms.