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Saliency guided local and global descriptors for effective action recognition

Abdulmunem, Ashwan, Lai, Yukun and Sun, Xianfang 2016. Saliency guided local and global descriptors for effective action recognition. Computational Visual Media 2 (1) , pp. 97-106. 10.1007/s41095-016-0033-9

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Abstract

This paper presents a novel framework for human action recognition based on salient object detection and a new combination of local and global descriptors. We first detect salient objects in video frames and only extract features for such objects. We then use a simple strategy to identify and process only those video frames that contain salient objects. Processing salient objects instead of all frames not only makes the algorithm more efficient, but more importantly also suppresses the interference of background pixels. We combine this approach with a new combination of local and global descriptors, namely 3D-SIFT and histograms of oriented optical flow (HOOF), respectively. The resulting saliency guided 3D-SIFT–HOOF (SGSH) feature is used along with a multi-class support vector machine (SVM) classifier for human action recognition. Experiments conducted on the standard KTH and UCF-Sports action benchmarks show that our new method outperforms the competing state-of-the-art spatiotemporal feature-based human action recognition method

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information: First online: 29 January 2016 This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution 4.0 International License
Publisher: Springer
ISSN: 2096-0433
Funders: Iraqi Ministry of Higher Education and Scientific Research (MHESR).
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 9 December 2015
Last Modified: 17 Oct 2019 02:45
URI: http://orca-mwe.cf.ac.uk/id/eprint/86214

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