Feature Detector and Descriptor Evaluation in Human Action Recognition

Shao, Ling and Mattivi, Riccardo (2010) Feature Detector and Descriptor Evaluation in Human Action Recognition. In: 2010 ACM International Conference on Image and Video Retrieval. UNSPECIFIED, CHN, pp. 477-484. ISBN 978-1-4503-0117-6/10/07

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Abstract

In this paper, we evaluate and compare different feature detection and feature description methods for part-based approaches in human action recognition. Different methods have been proposed in the literature for both feature detection of space-time interest points and description of local video patches. It is however unclear which method performs better in the field of human action recognition. We compare, in the feature detection section, Dollar's method [18], Laptev's method [22], a bank of 3D-Gabor filters [6] and a method based on Space-Time Differences of Gaussians. We also compare and evaluate different descriptors such as Gradient [18], HOG-HOF [22], 3D SIFT [24] and an enhanced version of LBP-TOP [15]. We show the combination of Dollar's detection method and the improved LBP-TOP descriptor to be computationally efficient and to reach the best recognition accuracy on the KTH database.

Item Type: Book Section
Uncontrolled Keywords: bag of words,feature descriptors,feature detectors,human action recognition,lbp-top
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 16 Feb 2017 02:26
Last Modified: 22 Apr 2020 11:07
URI: https://ueaeprints.uea.ac.uk/id/eprint/62638
DOI: 10.1145/1816041.1816111

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