Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Persistent object tracking with randomized forests

Title: Persistent object tracking with randomized forests
Authors: Klinger, Tobias; Muhle, Daniel; Shortis, M.; Paparoditis, N.; Mallet, C.
Source: XXII ISPRS Congress, Technical Commission III ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XXXIX-B3
Publisher Information: Copernicus GmbH
Publication Year: 2012
Collection: Institutional Repository of Leibniz Universität Hannover
Subject Terms: Learning; Detection; Decision Support; Tracking; Real-time; Video; trees; recognition; ddc:550; Konferenzschrift
Description: Our work addresses the problem of long-term visual people tracking in complex environments. Tracking a varying number of objects entails the problem of associating detected objects to tracked targets. To overcome the data association problem, we apply a Tracking-by-Detection strategy that uses Randomized Forests as a classifier together with a Kalman filter. Randomized Forests build a strong classifier for multi-class problems through aggregating simple decision trees. Due to their modular setup, Randomized Forests can be built incrementally, which makes them useful for unsupervised learning of object features in real-time. New training samples can be incorporated on the fly, while not drifting away from previously learnt features. To support further analysis of the automatically generated trajectories, we annotate them with quality metrics based on the association confidence. To build the metrics we analyse the confidence values that derive from the Randomized Forests and the similarity of detected and tracked objects. We evaluate the performance of the overall approach with respect to available reference data of people crossing the scene. ; DFG/HE 1822/24-1
Document Type: article in journal/newspaper
Language: English
ISBN: 978-1-62993-366-5; 1-62993-366-X
ISSN: 1682-1750
Relation: ESSN:2194-9034; http://dx.doi.org/10.15488/1094
DOI: 10.15488/1094
Availability: http://www.repo.uni-hannover.de/handle/123456789/1118; https://doi.org/10.15488/1094
Rights: CC BY 3.0 Unported ; https://creativecommons.org/licenses/by/3.0/ ; frei zugänglich
Accession Number: edsbas.A3906F10
Database: BASE