NDLI: Automatic initialization and tracking using attentional mechanisms

Content Provider	IEEE Xplore Digital Library
Author	Mahadevan, V. Vasconcelos, N.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, University of California, San Diego (Mahadevan, V.; Vasconcelos, N.)
Abstract	A biologically inspired approach for automated visual tracking is proposed. In this approach it is hypothesized that target initialization and tracking are a consequence of saliency mechanisms that guide the deployment of visual attention. The recently proposed discriminant center-surround saliency model, is used to derive the tracking framework. In this framework, automatic tracker initialization is achieved using bottom-up saliency with motion features, while the tracking problem is formulated as one of continuous target-background classification, implemented using saliency in two stages. The first, or learning stage, combines a focus of attention mechanism and bottom-up saliency to identify a maximally discriminant set of features for target detection. The second, or detection stage, uses a feature based attention mechanism and a target-tuned top-down discriminant saliency detector, to detect the target. Overall, the tracker iterates between learning discriminant features from the target location in a video frame and detecting the location of the target in the next frame. To implement this tracker, well known properties of the statistics of natural images are exploited leading to computational efficiency. Experimental results comparing the proposed method to the state of the art in tracking are presented, showing improved performance.
Starting Page	15
Ending Page	20
File Size	975731
Page Count	6
File Format	PDF
ISBN	9781457705298
ISSN	21607516
e-ISBN	9781457705304
DOI	10.1109/CVPRW.2011.5981782
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Target tracking Object detection Feature extraction Biology Robustness
Content Type	Text
Resource Type	Article

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