Speeding Up Semi-Supervised On-line Boosting for Tracking
Martin Godec, Helmut Gräbner, Christian Leistner, Horst Bischof · 2009
Recently, object tracking by detection using adaptive on-line classifiers has been investigated. In this case, the tracking problem is reduced to the discrimination of the current object view from the local background. However, on-line learning may introduce errors, which causes drifting and let the tracker fail. This can be avoided by using semi-supervised on-line learning (i.e., the use of labeled and unlabeled training samples), which allows to limit the drifting problem while still staying adaptive to appearance changes, in order to stabilize tracking. In particular, this paper extends semi-supervised on-line boosting by a particle filter to achieve a higher frame-rate. Furthermore, a more sophisticated search-space sampling, and an improved update sample selection have been added. In addition, a review of the semi-supervised on-line boosting algorithm is given and further experiments have been accomplished.