IoT+Small Data: Transforming in-store shopping analytics &amp; services

Radhakrishnan, Meera; Sen, Sougata; Vigneshwaran, S.; Misra, Archan; Balan, Rajesh Krishna

doi:10.1109/comsnets.2016.7439946

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Additionally, they use a smartglasses' gyroscope to track the user's head movement and the WiFi interface for the indoor location. The authors of [35] conduct several experiments with smartwatches to evaluate the feasibility of detection object picking gestures and object localization within a shelf using smartwatches. The authors also propose novel methods to track object picking with BLE devices (i.e., locate a beacon on a sliding door to track the door opening direction).…”

Section: Related Workmentioning

confidence: 99%

EyeShopper

Bermejo

Chatzopoulos

Hui

2020

Proceedings of the 28th ACM International Conference on Multimedia

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Recent advances in machine and deep learning allow for enhanced retail analytics by applying object detection techniques. However, existing approaches either require laborious installation processes to function or lack precision when the customers turn their back in the installed cameras. In this paper, we present EyeShopper, an innovative system that tracks the gaze of shoppers when facing away from the camera and provides insights about their behavior in physical stores. EyeShopper is readily deployable in existing surveillance systems and robust against low-resolution video inputs. At the same time, its accuracy is comparable to state-of-the-art gaze estimation frameworks that require high-resolution and continuous video inputs to function. Furthermore, EyeShopper is more robust than state-of-the-art gaze tracking techniques for back head images. Extensive evaluation with different real video datasets and a synthetic dataset we produced shows that EyeShopper estimates with high accuracy the gaze of customers.

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Section: Related Workmentioning

confidence: 99%

EyeShopper

Bermejo

Chatzopoulos

Hui

2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…Radhakrishnan et al [91] propose an architecture where lowcost BLE beacons and embedded sensors are mounted on product shelves, and their data is fused with sensor readings from a smartwatch worn by a shopper to create individualized services for in-store shoppers. This system can recognize some shopping activities, including picking item from a shelf, putting it in a trolley and interacting with shelves (opening and closing) in a store.…”

Section: C: Combination Of In-store Deployed Devices and Human Wearablesmentioning

confidence: 99%

“…However, continuous video is extremely expensive in terms of energy consumption, they rely on inertial sensors as much as possible and to trigger vision only when necessary. Radhakrishnan et al [91] aim to use infrastructure sensors, and wearable/mobile devices to detect customers' in-store behaviors in an energy-efficient manner. They propose an architecture where low-cost BLE beacons and embedded sensors are mounted on product shelves, and their data is fused with sensor readings from a smartwatch worn by a shopper.…”

Section: ) Pervasive Sensingmentioning

confidence: 99%

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

et al. 2019

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Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced usergenerated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI.

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“…The Process of BLE Beacon technique for in-store analytics [14] The above system is studied and analyzed this inspired us to design and develop an application which achieve the goal in the Internet of Things for improving customer shopping experience at stores.…”

Section: Figurementioning

confidence: 99%

“…The system for the shopping has been proposed by Meera Radhakrishnan which illustrates the use of IoT in retail stores [14] mainly by using below tools and techniques. Following diagram illustrate the flow of the process used.…”

Section: Ble Beacon System Architecturementioning

confidence: 99%