@INPROCEEDINGS{1368Pätzold2012, AUTHOR = {Michael Pätzold and Rubén Heras Evangelio and Thomas Sikora}, TITLE = {Boosting Multi-Hypothesis Tracking by means of Instance-specific Models}, BOOKTITLE = {9th IEEE International Conference on Advanced Video and Signal-Based Surveillance}, YEAR = {2012}, MONTH = sep, ADDRESS = {Beijing, China}, NOTE = {ISBN: 978-1-4673-2499-1}, PDF = {http://elvera.nue.tu-berlin.de/files/1368Pätzold2012.pdf}, DOI = {10.1109/AVSS.2012.18}, URL = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6328050&contentType=Conference+Publications&refinements%3D4284309875%2C4293333990%26searchField%3DSearch_All%26queryText%3Davss+2012}, ABSTRACT = {In this paper we present a visual person tracking-by-detection system based on on-line-learned instance-specific information along with the kinematic relation of measurements provided by a generic person-category detector. The proposed system is able to initialize tracks on individual persons and start learning their appearance even in crowded situations and does not require that a person enters the scene separately. For that purpose we integrate the process of learning instance-specific models into a standard MHT-framework. The capability of the system to eliminate detections-to-object association ambiguities occurring from missed detections or false ones is demonstrated by experiments for counting and tracking applications using very long video sequences on challenging outdoor scenarios.} }