Human action recognition using deep rule-based classifier

Allah Bux Sargano, Xiaowei Gu, Plamen Angelov, Zulfiqar Habib

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
92 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)30653–30667
Number of pages15
JournalMultimedia Tools and Applications
Issue number41-42
Early online date17 Aug 2020
Publication statusPublished - 01 Nov 2020
Externally publishedYes


  • Deep learning
  • Fuzzy rule-based classifier
  • Human action recognition


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