## 2 Literature Review  
Smart systems for shopping applications were recently targeted in many types of research that provide potential solutions for different impediments that apply to ordinary customers or those who require certain assistance. Those applications vary from smart shopping cards to smart scanning systems for visually impaired customers, smart assistants' software for TV shopping, or even for the analysis of customers' patterns and behaviors within stores [2,3]. The shopping cart developed in [4] aimed to provide customers with an autonomous payment service by eliminating the need for customers to wait in check-out queues or to manually search for and scan the barcodes of the items that they wish to purchase. This was accomplished through an object detection system built to detect and classify the different items added to the cart, seen by a camera module. Purchase validation was mainly dependent on load cells equipment with a load amplifier to facilitate the communication between the cells and the used microprocessor. Table 1 shows the features presented by different approaches, including ours. The approaches of [5,6] propose autonomously controlled robots to approach and select items. The carts presented by [6, 7, 8] use RFID technology for self-checkout; CNN is used for item detection inside the cart in [4]. From the point of view of the store, RFID technology increases the store infrastructure needed in terms of purchasing and installing scanning tags. Similarly, utilizing CNN for item detection would require a team specialized in constant training of the network with the new data presented by new items and appearance changes of products. The approach presented by [9] uses computer vision and the YOLO network to detect the different behaviors of shopping, including no hand, empty hand and holding item. This approach has neglected the option of holding multiple items, which, from the store's point of view, affects its reliability. The most common method for item validation is performed using weight sensors. Our proposed approach aims to provide the shopping experience with minimal requirements from the store, as using the YOLO  
model for barcode detection translates the process performed by a human cashier and is supported by a sensor and vision-based anti-theft system to increase reliability. Items detection was also used in other recent smart shopping systems, such as Caper AI Checkout and Amazon Go. Caper is a shopping cart that utilizes an object detection network to provide information on items detected by a camera module built in the cart. Item validation is achieved through an additional layer of recognition through sensor fusion and a weight sensing system. Caper cart can be considered an alternative for the Amazon Go system, which was launched earlier with the concept of checkout-free stores. Amazon Go is based only on a complicated object detection system that utilizes computer vision, deep learning, and sensor fusion technology with the help of a network of cameras and sensors installed within the store ceiling. This system not only allows the recognition of the items, but it also tracks all items, whether they are added to the baskets or returned to the shelves. It also tracks and distinguishes which user commits the purchasing or returning action as long as the user is in the store [10]. This approach does not require any additional validation system, but it is applied in relatively small grocery stores owned by Amazon, and it is not easily applicable in other stores. Though Caper carts also require training on the items provided in each store it is applied in, it still provides a manual scanning feature that makes it usable without training. Nevertheless, detecting grocery items can be problematic because it requires the detection model to be updated with the continuously changing packaging of different items in a store.