Customer Gaze Estimation in Retail Using Deep Learning
Customer Gaze Estimation in Retail Using Deep Learning
复制标题
使用深度学习进行零售业顾客注视估计
DOI：
10.1109/access.2022.3183357
复制
发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Jayarathna, Sampath
中科院分区：
文献类型：
--
作者：
Senarath, Shashimal;Pathirana, Primesh;Meedeniya, Dulani;Jayarathna, Sampath
目前，智能计算应用广泛应用于不同的领域，包括零售商店。对顾客行为的分析对顾客和零售商的利益都至关重要。在这方面，使用深度学习的远程凝视估计概念在分析零售客户行为方面显示出有希望的结果，因为它具有可扩展性，鲁棒性，低成本和不间断性。本研究提出了一种基于三阶段、三注意的深度卷积神经网络，用于零售业图像数据的远程凝视估计。在第一阶段，我们设计了一种利用图像数据和单目深度估计来估计受试者三维凝视的机制。第二阶段提出了一种新的三注意机制，从视野、深度范围和物体通道的注意来估计野外的注视。第三阶段根据第二阶段的输出注意图生成凝视显著性热图。我们使用基准GOO-Real数据集训练和评估所提出的模型，并将结果与基线模型进行比较。此外，我们通过引入一个新的零售凝视数据集，使我们的模型适应真实的零售环境。大量的实验表明，我们的方法显著提高了在GOO-Real和Retail凝视数据集上的远程凝视目标估计性能。
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.