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Sparse representation-based human detection: a scale-embedded dictionary approach

Vinay, Krishna G and Haque, SM and Babu, Venkatesh R and Ramakrishnan, KR (2016) Sparse representation-based human detection: a scale-embedded dictionary approach. In: SIGNAL IMAGE AND VIDEO PROCESSING, 10 (3, SI). pp. 585-592.

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Official URL: http://dx.doi.org/10.1007/s11760-015-0781-5

Abstract

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.

Item Type: Journal Article
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Additional Information: Copy right for this article belongs to the SPRINGER LONDON LTD, 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
Keywords: Human detection; Histogram of oriented gradients (HOG); l(1)-Norm minimization; Sparse representation; Sparse classification; Scale-embedded dictionary
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Division of Information Sciences > Supercomputer Education & Research Centre
Date Deposited: 02 Apr 2016 07:35
Last Modified: 02 Apr 2016 07:35
URI: http://eprints.iisc.ernet.in/id/eprint/53544

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