Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

On-line Multi-View Forests for Tracking ⋆

Abstract. 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.