The dataset for a vision-based unmanned kiosk

In recent years, many unmanned retail stores have been introduced with the development of computer vision, sensors and wireless technology. However, the trend has slowed due to the costs associated with implementation. A vision-based kiosk, which is only equipped with a vision sensor, can be a compact and affordable option. The vision-based kiosk detects a product via CNN-based object detectors and performs instance-level classification by aggregating detections from multiple cameras. There are many public datasets for retail object recognition. However, the products that appear in the dataset are captured under certain conditions, such as a white background, on the shelf, and a pack shot. We provide a dataset which is constructed solely for the vision-based unmanned kiosk. The dataset consists of 115k images collected with the prototype unmanned kiosk system. In the system, multiple cameras are installed to capture an object from various viewpoints. For the actual purchasing process, retail products are selected and returned by customers in the scene

近年来，随着计算机视觉、传感技术及无线通信技术的进步，众多无人零售商店相继问世。然而，由于实施过程中的成本问题，这一趋势有所放缓。基于视觉的自动售货机，仅配备视觉传感器，便成为了一种紧凑且经济实惠的选择。该视觉自动售货机通过基于卷积神经网络（CNN）的对象检测器检测商品，并通过整合多台摄像机的检测结果，实现实例级别的分类。目前，已有众多面向零售物体识别的公共数据集。然而，数据集中的产品是在特定条件下拍摄的，如白色背景、货架摆放和包装照片。本数据集专为基于视觉的无人售货机构建，包含由原型无人售货机系统采集的11.5万张图像。在该系统中，多台摄像机被安装以从不同角度捕捉物体。在实际购买过程中，客户在场景中选择和归还零售产品。