Paper Info
Title
Human action recognition using deep rule-based classifier.
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.
Year
DOI
Venue
2020
10.1007/s11042-020-09381-9
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Human action recognition,Deep learning,Fuzzy rule-based classifier
Journal
79.0
Issue
ISSN
Citations 
41-42
1380-7501
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Allah Bux Sargano101.35
Xiaowei Gu29910.96
Plamen Angelov395467.44
Zulfiqar Habib49014.60