Customer Gaze Estimation in Retail Using Deep Learning

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.