Gaze-FTNet: a feature transverse architecture for predicting gaze attention

Srijith Rajeev; Shreyas Kamath K. M.; Abigail Stone; Karen Panetta; Sos S. Agaian

doi:10.1117/12.2618989

27 May 2022 Gaze-FTNet: a feature transverse architecture for predicting gaze attention

Srijith Rajeev, Shreyas Kamath K. M., Abigail Stone, Karen Panetta, Sos S. Agaian

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000N (2022) https://doi.org/10.1117/12.2618989
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

The dynamics of gaze coordination in natural contexts are affected by various properties of the task, the agent, the environment, and their interaction. Artificial Intelligence (AI) lays the foundation for detection, classification, segmentation, and scene analysis. Much of AI in everyday use is dedicated to predicting people's behavior. However, a purely data-driven approach cannot solve development problems alone. Therefore, it is imperative that decision-makers also consider another AI approach—causal AI, which can help identify the precise relationships of cause and effect. This article presents a novel Gaze Feature Transverse Network (Gaze-FTNet) that generates close-to-human gaze attention. The proposed end-to-end trainable approach leverages a feature transverse network (FTNet) to model long-term dependencies for optimal saliency map prediction. Moreover, several modern backbone architectures are explored, tested, and analyzed. Synthetically predicting human attention from monocular RGB images will benefit several domains, particularly humanvehicle interaction, autonomous driving, and augmented reality.

Citation Download Citation

Srijith Rajeev, Shreyas Kamath K. M., Abigail Stone, Karen Panetta, and Sos S. Agaian "Gaze-FTNet: a feature transverse architecture for predicting gaze attention", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000N (27 May 2022); https://doi.org/10.1117/12.2618989

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