Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services

Sarwar, M.; Daraghmi, Yousef‐Awwad; Liu, Kuan-Wen; Chi, Hong-Chuan; Ik, Tsi Ui; Li, Yih-Lang

doi:10.1109/wcnc45663.2020.9120574

Cited by 18 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virtual Cart is a lightweight system of low-cost solution which is suitable for the small-scale retail stores. The system is evaluated using real world checkout video, and the accuracy of the shopping event detection and item recognition is about 97% [14]. The app also generates QR codes for swift payments, streamlining the checkout process.…”

Section: Android (Java)mentioning

confidence: 99%

Swiftcart: Revolutionizing Shopping Adventure using Image Processing and Cloud Computing with Internet of Things

et al. 2024

IJFMR

View full text Add to dashboard Cite

show abstract

Section: Android (Java)mentioning

confidence: 99%

Swiftcart: Revolutionizing Shopping Adventure using Image Processing and Cloud Computing with Internet of Things

et al. 2024

IJFMR

View full text Add to dashboard Cite

show abstract

“…As we know that there are so many types of microcontroller families that are available in the market. In order to fulfill our application ATmega328 controller is enough [16]. While, there are many sensors that are being used in this project such as RFID tags, buzzer, Bluetooth module, and load cell.…”

Section: Circuit Descriptionmentioning

confidence: 99%

A radio frequency identification based smart shopping trolley system for automated billing

Kamil Naji,

Desher Farhood,

Fadhil Fahad

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

On holidays and weekends, we can notice a large rush at shopping centres in metro areas. This is exacerbated when there are substantial discounts and deals. By applying the notion of internet of thing (IoT) for concerning all things in the grocery store, an automated smart shopping system is created. Each product within such scheme had an economical radio frequency identification (RFID) tag positioned with it. Once an item has been added to a smart cart, the information about the merchandise is quickly read by the RFID reader on the cart. The outcome is, direct invoicing is done out from shopping cart, saving clients from having to wait in a long line at the checkout. The product's expiration date is also displayed and broken products can be determined by their weight. As a result, expired and damaged products will not be taken into account when calculating the cost. Furthermore, this system gains the addition of smart shelves incorporating RFID readers which may check stock and possibly update a central server. Inventory control becomes simpler as a result. Finally, cashier stations could verify a customer's purchase. As a result, billing may be done right in the trolley, saving clients a lot of time.

show abstract

“…Currently, the rise of ambient intelligence is leading to a change in everyday environments towards connected spaces [14]. Amongst these ambient intelligence technologies are systems for tracking and identifying individuals in their natural environment, (e.g., shopping malls [15], health care centers [16], manufactures [17], or at home [18]), in order to anticipate individual needs and provide personalized services. Intelligent floors are often developed in order to recognize when elderly people fall, to prevent serious accidents, and to allow a quick intervention of emergency services and staff.…”

Section: Related Workmentioning

confidence: 99%

Extracting Walking Trajectories at Home From a Capacitive Proximity Sensing Floor

Sannier¹,

Janaqi²,

Raducanu³

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

Walking at home can provide valuable information about locomotor efficiency, anticipation of daily hazards and general wellbeing. Here, we present a multidisciplinary method to reconstruct locomotor trajectories while walking at home with a capacitive proximity sensing devicethe SensFloorwhich was installed in a real occupied apartment in the city center of Montpellier in France. Our recognition method is based on the spatio-temporal statistical probability of body location corresponding to sensors' activation. The results led to the localization of the inhabitant in the two-dimensional floor space, and their tracking over a 24-hour period. More precisely, our technique enabled us to distinguish human-related behavior from the location of static objects. It also allowed us to successfully identify locomotor trajectories in a highly confined space, including those from two simultaneously walking individuals in different rooms. It allowed us to obtain valuable information on spatial behavior (trajectory, stationarity) but also on temporal behavior (occupancy time, walking duration). As this technique compensates for the already established low accuracy of capacitive sensors, our method offers innovative possibilities to study locomotor metrics at home using relatively inexpensive sensing technology.

show abstract

Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services

Cited by 18 publications

References 15 publications

Swiftcart: Revolutionizing Shopping Adventure using Image Processing and Cloud Computing with Internet of Things

Swiftcart: Revolutionizing Shopping Adventure using Image Processing and Cloud Computing with Internet of Things

A radio frequency identification based smart shopping trolley system for automated billing

Extracting Walking Trajectories at Home From a Capacitive Proximity Sensing Floor

Contact Info

Product

Resources

About