Volltext-Downloads (blau) und Frontdoor-Views (grau)

Fusion of tracking techniques to enhance adaptive real-time tracking of arbitrary objects

  • In visual adaptive tracking, the tracker adapts to the target, background, and conditions of the image sequence. Each update introduces some error, so the tracker might drift away from the target over time. To increase the robustness against the drifting problem, we present three ideas on top of a particle filter framework: An optical-flow-based motion estimation, a learning strategy for preventing bad updates while staying adaptive, and a sliding window detector for failure detection and finding the best training examples. We experimentally evaluate the ideas using the BoBoT dataseta. The code of our tracker is available online.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author of HS ReutlingenRätsch, Matthias
URN:urn:nbn:de:bsz:rt2-opus4-1015
DOI:https://doi.org/10.1016/j.procs.2014.11.025
eISSN:1877-0509
Erschienen in:Procedia computer science
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of Publication:2014
Tag:adaptive tracking; optical flow; particle filter; sliding window
Volume:39
Issue:6th International conference on Intelligent Human Computer Interaction, IHCI 2014
Page Number:4
First Page:162
Last Page:165
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open Access?:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, keine Bearbeitung