Supervised learning for Out-of-Stock detection in panoramas of retail shelves

Rosado, Luís; Gonçalves, João; Costa, Joao Guilherme Cavalcanti; Ribeiro, David; Soares, Filipe

doi:10.1109/ist.2016.7738260

Cited by 25 publications

(13 citation statements)

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“…The soil particles distribution is used for soil classification and thus serves as the estimator for water holding capacity and drainage, aeration, susceptibility to erosion and cation exchange capacity, pH buffering capacity. Alternatively, using the library Texture-Color-Geometry Feature Extraction (TCGFE) [ 36 , 37 , 38 ] developed by Fraunhofer Portugal AICOS, a total of 152 features can be extracted for each region of interest in the image, which can later be used for machine learning purposes. Alternatively, texture can also be indirectly measured by fractal dimension [ 39 ].…”

Section: Proposed Iot Sensing Platformmentioning

confidence: 99%

IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

Oliveira-Jr

Resende

Pereira

et al. 2020

Sensors

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Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income.

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Section: Proposed Iot Sensing Platformmentioning

confidence: 99%

IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

Oliveira-Jr

Resende

Pereira

et al. 2020

Sensors

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“…More recently, computer vision systems have been proposed by many as a promising solution for smart retail applications including detection of misplaced products [11], verification of planogram compliance [6], and stock assessment [12]. Cameras for data gathering can be installed at fixed locations in the store [6], or integrated into smartphones [13], manually-driven carts [12] or on-board mobile robotic platforms [14]. Image processing techniques mainly using state-of-the-art feature-based or template-based matching algorithms have been adopted to look for anomalies and trigger alarms for store managers.…”

Section: Related Workmentioning

confidence: 99%

“…In order to cope with the high variability of store environments, the use of machine learning techniques has been proposed, to ensure higher robustness and accuracy, although at the cost of labor-intensive manual image annotation for training. An example can be found in [12], where a supervised learning approach based on Support Vector Machines (SVM) is developed for OOS detection in panoramic images of retail shelves. The camera image setup includes a camera cart that is moved parallel to the shelf to acquire images from multiple viewpoints.…”

Section: Related Workmentioning

confidence: 99%

Towards Intelligent Retail: Automated on-Shelf Availability Estimation Using a Depth Camera

et al. 2020

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Efficient management of on-shelf availability and inventory is a key issue to achieve customer satisfaction and reduce the risk of profit loss for both retailers and manufacturers. Conventional store audits based on physical inspection of shelves are labor-intensive and do not provide reliable assessment. This paper describes a novel framework for automated shelf monitoring, using a consumer-grade depth sensor. The aim is to develop a low-cost embedded system for early detection of out-of-stock situations with particular regard to perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. The proposed solution exploits 3D point cloud reconstruction and modelling techniques, including surface fitting and occupancy grids, to estimate product availability, based on the comparison between a reference model of the shelf and its current status. No a priori knowledge about the product type is required, while the shelf reference model is automatically learnt based on an initial training stage. The output of the system can be used to generate alerts for store managers, as well as to continuously update product availability estimates for automated stock ordering and replenishment and for e-commerce apps. Experimental tests performed in a real retail environment show that the proposed system is able to estimate the on-shelf availability percentage of different fresh products with a maximum average discrepancy with respect to the actual one of about 5.0%. INDEX TERMS Automated stock monitoring, intelligent retail, RGB-D sensors, 3D reconstruction and modeling.

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“…Methods [3,4] have recently been proposed to monitor the shelves by using the images for retail stores. Kejriwal et al [3] proposed a method to directly obtain product count from images using mobile robots for stock monitoring and assessment in retail stores.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it requires a large database of high-quality product templates, which is labor-intensive to maintain since products frequently change due to new and seasonal products arriving. Rosado et al [4] proposed a method to detect out-of-stock regions in grocery retail shelves without using a large database. This method detects out-of-stock regions using points of interest (keypoints) detected by FAST (Features from Accelerated Segment Test) [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Shelf Monitoring Using Supervised Learning for Improving On-Shelf Availability in Retail Stores

Higa

Iwamoto

2019

Sensors

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This paper proposes a method to robustly monitor shelves in retail stores using supervised learning for improving on-shelf availability. To ensure high on-shelf availability, which is a key factor for improving profits in retail stores, we focus on understanding changes in products regarding increases/decreases in product amounts on the shelves. Our method first detects changed regions of products in an image by using background subtraction followed by moving object removal. It then classifies the detected change regions into several classes representing the actual changes on the shelves, such as “product taken (decrease)” and “product replenished/returned (increase)”, by supervised learning using convolutional neural networks. It finally updates the shelf condition representing the presence/absence of products using classification results and computes the product amount visible in the image as on-shelf availability using the updated shelf condition. Three experiments were conducted using two videos captured from a surveillance camera on the ceiling in a real store. Results of the first and second experiments show the effectiveness of the product change classification in our method. Results of the third experiment show that our method achieves a success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can maintain high on-shelf availability, enabling retail stores to increase profits.

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Supervised learning for Out-of-Stock detection in panoramas of retail shelves

Cited by 25 publications

References 10 publications

IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

Towards Intelligent Retail: Automated on-Shelf Availability Estimation Using a Depth Camera

Robust Shelf Monitoring Using Supervised Learning for Improving On-Shelf Availability in Retail Stores

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