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Boosting Multi-Hypothesis Tracking by means of Instance-specific Models
Citation key 1368Pätzold2012
Author Michael Pätzold and Rubén Heras Evangelio and Thomas Sikora
Title of Book 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance
Year 2012
DOI 10.1109/AVSS.2012.18
Address Beijing, China
Month sep
Note ISBN: 978-1-4673-2499-1
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.
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