@conference {withopf2007a,
	title = {Improved training algorithm for tree-like classifiers and its application to vehicle detection},
	booktitle = {Proc. IEEE Intelligent Transportation Systems Conference (ITSC)},
	year = {2007},
	pages = {642--647},
	abstract = {We propose a new training algorithm for tree classifiers and cascades for object detection and compare it to a standard algorithm for cascade training. Our experiments show that the proposed algorithm significantly reduces the number of features needed per stage by incorporating the output of the previous stage as a weak learner into the next stage. This approach also speeds up the classification while maintaining the same detection accuracy. The analysis of the features selected by the algorithm provides further insights into its functioning.},
	doi = {10.1109/ITSC.2007.4357644},
	author = {Daniel Withopf and Bernd J{\"a}hne}
}
@phdthesis {withopf2007,
	title = {Reliable Real-Time Vehicle Detection and Tracking},
	year = {2007},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ.\ Heidelberg},
	url = {http://d-nb.info/98745398X},
	author = {Daniel Withopf}
}
@conference {withopf2006,
	title = {Learning algorithm for real-time vehicle tracking},
	booktitle = {Proc. IEEE Intelligent Transportation Systems Conference ITSC {\textquoteright}06},
	year = {2006},
	pages = {516--521},
	abstract = {This article presents a learning algorithm for real-time object tracking in video sequences which uses an improvement of a feature selection method known from object detection. But in contrast to trackers based on object detection methods, our approach explicitly selects the features which are best suited to track an object, which are different from the best features for object detection. The used features are constructed from pairs of image patches and related to Haar features. Besides the automatic selection of features according to their discriminative (tracking) power, the advantage of this approach is that the resulting tracker is very fast, allowing it to run in addition to a detector to robustify the object position estimation and to compensate for dropouts of the detector. A comparison of the proposed tracking algorithm with other tracking methods is presented which shows the accuracy of the proposed algorithm},
	doi = {10.1109/ITSC.2006.1706793},
	author = {Daniel Withopf and Bernd J{\"a}hne}
}
