Recognition of Customer Behavior on the Front of Shelf from Surveillance Camera

• Item type: SIG Technical Reports(1)
• 公開日: 2015-09-07
• タイトル: Recognition of Customer Behavior on the Front of Shelf from Surveillance Camera
• タイトル / 言語: en
• タイトル: Recognition of Customer Behavior on the Front of Shelf from Surveillance Camera
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Institute of Industrial Science, The University of Tokyo
• 著者所属: Institute of Industrial Science, The University of Tokyo
• 著者所属: Institute of Industrial Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Institute of Industrial Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Institute of Industrial Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Institute of Industrial Science, The University of Tokyo
• 著者名: Jingwen, Liu; Yanlei, Gu; Shunsuke, Kamijo
• 著者名(英): Jingwen, Liu; Yanlei, Gu; Shunsuke, Kamijo
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The analysis of customer behavior from surveillance camera is one of the most important open topics for marketing. We propose a system to recognize different customer behaviors on the front of shelf: no interest, viewing, turning to shelf, touching, picking and returning to shelf and picking and putting into basket, which show customer's increasing interest to products. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning to the shelf. Semi-Supervised Learning method is applied to optimize the training dataset and to generate an accurate classifier. As for the arm action recognition, a novel combined handfeature (CHF), which includes hand trajectory, tracking status and the relative position between hand and shopping basket, is proposed to describe different armactions. The CHF is classified by Dynamic Bayesian Network into different arm actions. A series of experiments demonstrate the effectiveness of the proposed methods and the performance to the developed system.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The analysis of customer behavior from surveillance camera is one of the most important open topics for marketing. We propose a system to recognize different customer behaviors on the front of shelf: no interest, viewing, turning to shelf, touching, picking and returning to shelf and picking and putting into basket, which show customer's increasing interest to products. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning to the shelf. Semi-Supervised Learning method is applied to optimize the training dataset and to generate an accurate classifier. As for the arm action recognition, a novel combined handfeature (CHF), which includes hand trajectory, tracking status and the relative position between hand and shopping basket, is proposed to describe different armactions. The CHF is classified by Dynamic Bayesian Network into different arm actions. A series of experiments demonstrate the effectiveness of the proposed methods and the performance to the developed system.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11131797
• 書誌情報: 研究報告コンピュータビジョンとイメージメディア（CVIM）; 巻 2015-CVIM-198, 号 18, p. 1-6, 発行日 2015-09-07
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8701
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会