@inproceedings{Bajramovic05:ECO,
    type = {inproceedings},
    key = {Bajramovic05:ECO},
    title = {Efficient Combination of Histograms for Real-Time Tracking Using Mean-Shift and Trust-Region Optimization},
    author = {Ferid Bajramovic and Christoph Gräßl and Joachim Denzler},
    booktitle = {Symposium of the German Association for Pattern Recognition (DAGM)},
    year = {2005},
    pages = {254-261},
    publisher = {Springer},
    abstract = {Histogram based real-time object tracking methods, like the Mean-Shift tracker of Comaniciu/Meer or the Trust-Region tracker of Liu/Chen, have been presented recently. The main advantage is that a suited histogram allows for very fast and accurate tracking of a moving object even in the case of partial occlusions and for a moving camera. The problem is which histogram shall be used in which situation. In this paper we extend the framework of histogram based tracking. As a consequence we are able to formulate a tracker that uses a weighted combination of histograms of different features. We compare our approach with two already proposed histogram based trackers for different histograms on large test sequences available to the public. The algorithms run in real-time on standard PC hardware.},
    groups = {tracking},
    website = {www.springerlink.com/content/2N6VJP31D3XA20JA},
}