Real-time human action recognition using stacked sparse autoencoders

Farooq, Adnan and Mohammad, Emad U Din and Ahmad Zarir, Abdullah and Ismail, Amelia Ritahani and Sulaiman, Suriani (2018) Real-time human action recognition using stacked sparse autoencoders. Indian Journal of Science and Technology, 11 (4). pp. 1-6. ISSN 0974-6846 E-ISSN 0974-5645

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Abstract

Objectives: In this paper, an automated real-time human and human-action detection system is developed using Histogram of Oriented Gradients (HOG) and Stacked Sparse Auto-encoders respectively. Methods: For human detection, a feature descriptor is trained using SVM classifier and then is used for identification of humans in the frames. Stacked Sparse autoencoders are a category of deep neural networks, and in the proposed work is used for the feature extraction of human actions from the human action video dataset. The extracted features represent a dictionary which is used to map the input and produce a linear combination, following that soft-max classification is applied to train the model. To reduce the computational complexity, input frames has been changed into binary temporal difference images and fed to the neural network. Analysis: The proposed model matched the other state of the art models applied for human-action recognition classification problems. Applications: The study reveals that using multiple layers can improve the classification performance: 75% with two-layers and 83% with three-layers model.

Item Type:	Article (Journal)
Additional Information:	4296/62341
Uncontrolled Keywords:	Auto-Encoder, HOG, Soft-Max, SVM
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	06 Mar 2018 10:06
Last Modified:	02 Jan 2024 10:25
URI:	http://irep.iium.edu.my/id/eprint/62341

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