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Probabilistic Multi-person Tracking Using Dynamic Bayes Networks : Volume Ii-3/W5, Issue 1 (20/08/2015)

By Klinger, T.

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Book Id: WPLBN0004013937
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Probabilistic Multi-person Tracking Using Dynamic Bayes Networks : Volume Ii-3/W5, Issue 1 (20/08/2015)  
Author: Klinger, T.
Volume: Vol. II-3/W5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Heipke, C., Rottensteiner, F., & Klinger, T. (2015). Probabilistic Multi-person Tracking Using Dynamic Bayes Networks : Volume Ii-3/W5, Issue 1 (20/08/2015). Retrieved from

Description: Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hannover, Germany. Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.



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