On-Line Multi-view Forests for Tracking

A successful approach to tracking is to on-line learn discriminative classifiers for the target objects. Although these trackingby-detection approaches are usually fast and accurate they easily drift in case of putative and self-enforced wrong updates. Recent work has shown that classifier-based trackers can be significantly stabilized by applying semi-supervised learning methods instead of supervised ones. In this paper, we propose a novel on-line multi-view learning algorithm based on random forests. The main idea of our approach is to incorporate multiview learning inside random forests and update each tree with individual label estimates for the unlabeled data. Our method is fast, easy to implement, benefits from parallel computing architectures and inherently exploits multiple views for learning from unlabeled data. In the tracking experiments, we outperform the state-of-the-art methods based on boosting and random forests.