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Fast and Reliable CAMShift Tracking

  • CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes),CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearance and the continuous monitoring of object identi- ties to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain an ecient GPU implementations of histogram generation, probability back projection, im- age moments computations, and histogram intersection. All of these techniques make full use of a GPU's high parallelization.show moreshow less

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Metadaten
Document Type:Report
Author: David Exner, Erich Bruns, Daniel Kurz, Anselm Grundhöfer, Oliver BimberORCiDGND
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.1410Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20091217-14962Cite-Link
Language:English
Date of Publication (online):2009/12/17
Year of first Publication:2009
Release Date:2009/12/17
Creating Corporation:JP AUgmented Reality, Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Medien / Junior-Professur Augmented Reality
Tag:CAMShift; GPU Programming; Kernel-Based Tracking
CAMShift; GPU Programming; Kernel-Based Tracking
GND Keyword:Bildverarbeitung
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
BKL-Classification:54 Informatik / 54.74 Maschinelles Sehen
Licence (German):License Logo Creative Commons 4.0 - Namensnennung-Nicht kommerziell (CC BY-NC 4.0)