A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

• Published in: Electronics (Basel) 2023-05, Vol.12 (10), p.2243
• Main Authors: Jadoon, Rab Nawaz, Nadeem, Aqsa, Shafi, Jawad, Khan, Muhammad Usman, ELAffendi, Mohammed, Shah, Sajid, Ali, Gauhar
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive systems; Algorithms; Ambient intelligence; Artificial neural networks; Attention; Cameras; Evolution; Eye movements; Flexibility; Forecasting techniques; Fuzzy algorithms; Fuzzy logic; Fuzzy systems; Information display systems; Iterative methods; Neural networks; Prediction theory; R&D; Real time; Research & development; Robustness (mathematics)
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12102243

This research paper presents the prediction of the visual attention area on a visual display using an evolving rule-based fuzzy model: evolving Takagi–Sugeno (eTS). The evolving fuzzy model is feasible for predicting the visual attention area because of its non-iterative, recursive, online, and real-time nature. Visual attention area prediction through a web camera is a problem that requires online adaptive systems with higher accuracy and greater performance. The proposed approach using an evolving fuzzy model to predict the eye-gaze attention area on a visual display in an ambient environment (to provide further services) mimics the human cognitive process and its flexibility to generate fuzzy rules without any prior knowledge. The proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (VAAPeNFS) approach can quickly generate compact fuzzy rules from new data. Numerical experiments conducted in a simulated environment further validate the performance and accuracy of the proposed model. To validate the model, the forecasting results of the eTS model are compared with DeTS and ANFIS. The result shows high accuracy, transparency and flexibility achieved by applying the evolving online versions compared to other offline techniques. The proposed approach significantly reduces the computational overhead, which makes it suitable for any sort of AmI application. Thus, using this approach, we achieve reusability, robustness, and scalability with better performance with high accuracy.