Human Action Recognition with Sparse Autoencoder and Histogram of Oriented Gradients


Lim, Kian Ming and Lee, Chin Poo and Tan, Pooi Shiang (2020) Human Action Recognition with Sparse Autoencoder and Histogram of Oriented Gradients. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 26-27 Sept. 2020, Kota Kinabalu, Malaysia.

[img] Text
Human Action Recognition with Sparse....pdf
Restricted to Repository staff only

Download (1MB)


This paper presents a video-based human action recognition method leveraging deep learning model. Prior to the filtering phase, the input images are pre-processed by converting them into grayscale images. Thereafter, the region of interest that contains human performing action are cropped out by a pre-trained pedestrian detector. Next, the region of interest will be resized and passed as the input image to the filtering phase. In this phase, the filter kernels are trained using Sparse Autoencoder on the natural images. After obtaining the filter kernels, convolution operation is performed in the input image and the filter kernels. The filtered images are then passed to the feature extraction phase. The Histogram of Oriented Gradients descriptor is used to encode the local and global texture information of the filtered images. Lastly, in the classification phase, a Modified Hausdorff Distance is applied to classify the test sample to its nearest match based on the histograms. The performance of the deep learning algorithm is evaluated on three benchmark datasets, namely Weizmann Action Dataset, CAD-60 Dataset and Multimedia University (MMU) Human Action Dataset. The experimental results show that the proposed deep learning algorithm outperforms other methods on the Weizmann Dataset, CAD-60 Dataset and MMU Human Action Dataset with recognition rates of 100%, 88.24% and 99.5% respectively.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Human action recognition, Human activity recognition
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 15 Sep 2021 07:22
Last Modified: 15 Sep 2021 07:22


Downloads per month over past year

View ItemEdit (login required)