Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store

• URL: http://arxiv.org/abs/2601.00928v1
• Date: Fri, 02 Jan 2026 01:40:12 GMT
• Title: Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store
• Authors: Luis Yoichi Morales, Francesco Zanlungo, David M. Woollard,
• Abstract summary: We introduce an algorithm that computes shoppers' shelf visits''<n> Shelf visits are extracted from trajectories obtained via machine vision-based 3D tracking and overhead cameras.<n>An analysis of the results shows that the algorithm can recognize customers' browsing activity when evaluated in an environment different from the one on which calibration was performed.
• Score: 0.5161531917413708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by recent challenges in the deployment of robots into customer-facing roles within retail, this work introduces a study of customer activity in physical stores as a step toward autonomous understanding of shopper intent. We introduce an algorithm that computes shoppers' ``shelf visits'' -- capturing their browsing behavior in the store. Shelf visits are extracted from trajectories obtained via machine vision-based 3D tracking and overhead cameras. We perform two independent calibrations of the shelf visit algorithm, using distinct sets of trajectories (consisting of 8138 and 15129 trajectories), collected in different stores and labeled by human reviewers. The calibrated models are then evaluated on trajectories held out of the calibration process both from the same store on which calibration was performed and from the other store. An analysis of the results shows that the algorithm can recognize customers' browsing activity when evaluated in an environment different from the one on which calibration was performed. We then use the model to analyze the customers' ``browsing patterns'' on a large set of trajectories and their relation to actual purchases in the stores. Finally, we discuss how shelf browsing information could be used for retail planning and in the domain of human-robot interaction scenarios.

Related papers

• Generating In-store Customer Journeys from Scratch with GPT Architectures [0.0]
  We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously. We trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models.
  arXiv  Detail & Related papers  (2024-07-13T12:35:52Z)
• Retail store customer behavior analysis system: Design and Implementation [2.215731214298625]
  We propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.
  arXiv  Detail & Related papers  (2023-09-05T06:26:57Z)
• Online Deep Clustering with Video Track Consistency [85.8868194550978]
  We propose an unsupervised clustering-based approach to learn visual features from video object tracks. We show that exploiting an unsupervised class-agnostic, yet noisy, track generator yields to better accuracy compared to relying on costly and precise track annotations.
  arXiv  Detail & Related papers  (2022-06-07T08:11:00Z)
• Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity [54.26540039514418]
  This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
  arXiv  Detail & Related papers  (2021-11-11T05:36:39Z)
• Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection [81.32280287658486]
  We propose a novel one-stage method, namely Glance and Gaze Network (GGNet) GGNet adaptively models a set of actionaware points (ActPoints) via glance and gaze steps. We design an actionaware approach that effectively matches each detected interaction with its associated human-object pair.
  arXiv  Detail & Related papers  (2021-04-12T08:01:04Z)
• OPAM: Online Purchasing-behavior Analysis using Machine learning [0.8121462458089141]
  We present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters.
  arXiv  Detail & Related papers  (2021-02-02T17:29:52Z)
• Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework [3.6726589459214445]
  The proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights. The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions.
  arXiv  Detail & Related papers  (2020-10-06T06:16:44Z)
• A robot that counts like a child: a developmental model of counting and pointing [69.26619423111092]
  A novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognition. The trained model is able to count a set of items and at the same time points to them.
  arXiv  Detail & Related papers  (2020-08-05T21:06:27Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)
• Towards in-store multi-person tracking using head detection and track heatmaps [11.318061963422807]
  We introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket. We propose a model for recognizing customers and staff based on their movement patterns. The model is evaluated using a real-world dataset collected in a supermarket over a 24-hour period that achieves 98% accuracy during training and 93% accuracy during evaluation.
  arXiv  Detail & Related papers  (2020-05-16T15:07:19Z)
• PeTra: A Sparsely Supervised Memory Model for People Tracking [50.98911178059019]
  We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots. We empirically compare key modeling choices, finding that we can simplify several aspects of the design of the memory module while retaining strong performance. PeTra is highly effective in both evaluations, demonstrating its ability to track people in its memory despite being trained with limited annotation.
  arXiv  Detail & Related papers  (2020-05-06T17:45:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.