Robust Object Tracking using Semi-Supervised Online Boosting

This work presents a detailed analysis and discussion of a new object tracking method using semi-supervised on-line boosting1. In order to avoid the drifting problem, which presents a challenge to adaptive tracking systems, the new approach incorporates prior knowledge of the tracked object into the tracking process via semi-supervised learning. This method makes it possible to distinguish between actual appearance changes of the object and changes that arise from erroneous measurements. Experiments show that this new method is very robust to misaligned object model updates and moreover performs on a similar performance level as other equivalent state-of-the-art object tracking methods. Within this thesis, the presented approach is analysed and optimised as well as extended to enable real-time performance. Semi-Supervised On-line Boosting for Robust Tracking, Grabner et al. [20]