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Information 2016, 7(4), 68; doi:10.3390/info7040068

A Discriminative Framework for Action Recognition Using f-HOL Features

1
Department of Math and Computer Science, Faculty of Science, Sohag University, 82524 Sohag, Egypt
2
Institute for Information Technology and Communications, Otto-von-Guericke-University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 30 August 2016 / Revised: 4 November 2016 / Accepted: 8 November 2016 / Published: 22 November 2016
(This article belongs to the Section Information and Communications Technology)
View Full-Text   |   Download PDF [457 KB, uploaded 24 November 2016]   |  

Abstract

Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vision tasks, in this paper, we present an innovative compact feature descriptor called fuzzy Histogram of Oriented Lines (f-HOL) for action recognition, which is a distinct variant of the HOG feature descriptor. The intuitive idea of these features is based on the observation that the slide area of the human body skeleton can be viewed as a spatiotemporal 3D surface, when observing a certain action being performed in a video. The f-HOL descriptor possesses an immense competitive advantage, not only of being quite robust to small geometric transformations where the small translation and rotations make no large fluctuations in histogram values, but also of not being very sensitive under varying illumination conditions. The extracted features are then fed into a discriminative conditional model based on Latent-Dynamic Conditional random fields (LDCRFs) to learn to recognize actions from video frames. When tested on the benchmark Weizmann dataset, the proposed framework substantially supersedes most existing state-of-the-art approaches, achieving an overall recognition rate of 98.2%. Furthermore, due to its low computational demands, the framework is properly amenable for integration into real-time applications. View Full-Text
Keywords: human action recognition; Histogram of Oriented Gradients (HOG); fuzzy Histogram of Oriented Lines (f-HOL); Latent-Dynamic Conditional random fields (LDCRFs); Weizmann action dataset human action recognition; Histogram of Oriented Gradients (HOG); fuzzy Histogram of Oriented Lines (f-HOL); Latent-Dynamic Conditional random fields (LDCRFs); Weizmann action dataset
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bakheet, S.; Al-Hamadi, A. A Discriminative Framework for Action Recognition Using f-HOL Features. Information 2016, 7, 68.

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