Journal
IEEE ACCESS
Volume 10, Issue -, Pages 64904-64919Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3183357
Keywords
Estimation; Three-dimensional displays; Channel estimation; Solid modeling; Adaptation models; Head; Videos; Computer vision; deep learning; gaze estimation; retail customer behaviour
Categories
Funding
- National Science Foundation CAREER [2045523]
- Department of Computer Science, Old Dominion University, Norfolk, Virginia, USA
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [2045523] Funding Source: National Science Foundation
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This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on different datasets.
At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data and monocular depth estimation. The second stage presents a novel three-attention mechanism to estimate the gaze in the wild from field-of-view, depth range, and object channel attentions. The third stage generates the gaze saliency heatmap from the output attention map of the second stage. We train and evaluate the proposed model using benchmark GOO-Real dataset and compare results with baseline models. Further, we adapt our model to real-retail environments by introducing a novel Retail Gaze dataset. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on GOO-Real and Retail Gaze datasets.
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