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Human action recognition using deep rule-based classifier

Sargano, Allah Bux, Gu, Xiaowei, Angelov, Plamen, Habib, Zulfiqar (2020) Human action recognition using deep rule-based classifier. Multimedia Tools and Applications, 79 (41-42). pp. 30653-30667. ISSN 1380-7501. (doi:10.1007/s11042-020-09381-9) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90402)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/s11042-020-09381-9

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: Article
DOI/Identification number: 10.1007/s11042-020-09381-9
Uncontrolled keywords: Human action recognition; Deep learning; Fuzzy rule-based classifier
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 10:10 UTC
Last Modified: 29 Sep 2021 11:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90402 (The current URI for this page, for reference purposes)
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