小売店舗における棚閲覧行動の解析：来店客の棚訪問推定アルゴリズム
Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store
店内カメラと3Dトラッキングで得た顧客軌跡から棚閲覧行動を推定するアルゴリズムを提案し、異なる店舗でも適用可能なことを示した。閲覧パターンと購買の関係も分析した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Luis Yoichi Morales, Francesco Zanlungo, David M. Woollard
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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.
