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Authors
Affiliations
1 Department of Computer Science, Kulliyyah of Information and Communication Technology,
International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, MY
Source
Indian Journal of Science and Technology, Vol 11, No 4 (2018), Pagination:
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.
Keywords
Auto-Encoder, HOG, Soft-Max, SVM
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