On-line Semi-supervised Multiple-Instance Boosting

A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot semi-supervised learning or multiple instance learning. Semi-supervised learning allows for incorporating priors and is more robust in case of occlusions while multiple-instance learning resolves the uncertainties where to take positive updates during tracking. In this work, we propose an on-line semi-supervised learning algorithm which is able to combine both of these approaches into a coherent framework. This leads to more robust results than applying both approaches separately. Additionally, we introduce a combined loss that simultaneously uses labeled and unlabeled samples, which makes ou...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Bernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof
• Keywords: Computer Vision, CVPR 2010, Multiple-Instance Learning, Semi-Supervised Boosting, Visual Object Tracking