Search Results (4)

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems. Full article

To provide analytic materials for business management for smart retail solutions, it is essential to recognize various customer behaviors (CB) from video footage acquired by in-store cameras. Along with frequent changes in needs and environments, such as promotion plans, product categories, in-store layouts, etc., the targets of customer behavior recognition (CBR) also change frequently. Therefore, one of the requirements of the CBR method is the flexibility to adapt to changes in recognition targets. However, existing approaches, mostly based on machine learning, usually take a great deal of time to re-collect training data and train new models when faced with changing target CBs, reflecting their lack of flexibility. In this paper, we propose a CBR method to achieve flexibility by considering CB in combination with primitives. A primitive is a unit that describes an object’s motion or multiple objects’ relationships. The combination of different primitives can characterize a particular CB. Since primitives can be reused to define a wide range of different CBs, our proposed method is capable of flexibly adapting to target CB changes in retail stores. In experiments undertaken, we utilized both our collected laboratory dataset and the public MERL dataset. We changed the combination of primitives to cope with the changes in target CBs between different datasets. As a result, our proposed method achieved good flexibility with acceptable recognition accuracy. Full article

Customer activity (CA) in retail environments, which ranges over various shopper situations in store spaces, provides valuable information for store management and marketing planning. Several systems have been proposed for customer activity recognition (CAR) from in-store camera videos, and most of them use machine learning based end-to-end (E2E) CAR models, due to their remarkable performance. Usually, such E2E models are trained for target conditions (i.e., particular CA types in specific store spaces). Accordingly, the existing systems are not malleable to fit the changes in target conditions because they require entire retraining of their specialized E2E models and concurrent use of additional E2E models for new target conditions. This paper proposes a novel CAR system based on a hierarchy that organizes CA types into different levels of abstraction from lowest to highest. The proposed system consists of multiple CAR models, each of which performs CAR tasks that belong to a certain level of the hierarchy on the lower level’s output, and thus conducts CAR for videos through the models level by level. Since these models are separated, this system can deal efficiently with the changes in target conditions by modifying some models individually. Experimental results show the effectiveness of the proposed system in adapting to different target conditions. Full article

Increasing rates of childhood obesity worldwide has focused attention on the obesogenic food environment. This paper reports an analysis of children’s interactions with food in convenience stores. Kids’Cam was a cross-sectional study conducted from July 2014 to June 2015 in New Zealand in which 168 randomly selected children aged 11–14 years old wore a wearable camera for a 4–day period. In this ancillary study, images from children who visited a convenience store were manually coded for food and drink availability. Twenty-two percent of children (n = 37) visited convenience stores on 62 occasions during the 4-day data collection period. Noncore items dominated the food and drinks available to children at a rate of 8.3 to 1 (means were 300 noncore and 36 core, respectively). The food and drinks marketed in-store were overwhelmingly noncore and promoted using accessible placement, price offers, product packaging, and signage. Most of the 70 items purchased by children were noncore foods or drinks (94.6%), and all of the purchased food or drink subsequently consumed was noncore. This research highlights convenience stores as a key source of unhealthy food and drink for children, and policies are needed to reduce the role of convenience stores in the obesogenic food environment. Full article