Human action recognition using deep rule-based classifier

Bux, Allah and Gu, Xiaowei and Angelov, Plamen and Habib, Zulfiqar (2020) Human action recognition using deep rule-based classifier. Multimedia Tools and Applications, 79 (41-42). pp. 30653-30667. ISSN 1380-7501

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Abstract

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples.

Item Type:
Journal Article
Journal or Publication Title:
Multimedia Tools and Applications
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-020-09381-9
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1705
Subjects:
ID Code:
149394
Deposited By:
Deposited On:
25 Nov 2020 16:05
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Jan 2021 13:58