NDLI: Boosting scalable gradient features for adaptive real-time tracking

Content Provider	IEEE Xplore Digital Library
Author	Klein, D.A. Cremers, A.B.
Copyright Year	2011
Description	Author affiliation: Intelligent Vision Systems Group, Department of Computer Science III, Rheinische Friedrich-Wilhelms-Universität Bonn, 53117, Germany (Klein, D.A.; Cremers, A.B.)
Abstract	Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the $BoBoT^{1}$ as well as the $PROST^{2}$ datasets.
Starting Page	4411
Ending Page	4416
File Size	557158
Page Count	6
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980369
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Target tracking Computational modeling Shape Training Reliability Real time systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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