Interest estimation method based on 2D pose features on security camera

Honma, Yuki; Togo, Ren; Abe, Maiku; Ogawa, Takahiro; Haseyama, Miki

doi:10.1117/12.2590676

Cited by 1 publication

(1 citation statement)

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“…As in the previous work, our research group has proposed a customer interest estimation method using a security camera in a real store 7 . In the method, for each frame in a certain video data, posture information is extracted for one target person based on a posture estimation model, and classify the presence or absence of interest of the target person using a neural network.…”

Section: Introductionmentioning

confidence: 99%

Customer interest estimation method in real store using re-identification and 3D posture estimation models

Yamashiro

Honma²,

Togo

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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This paper presents a novel Transformer-based customer interest estimation method using videos from a security camera in a real store. Expectations for the application of Artificial Intelligence (AI) technology in various industrial fields have increased. In this study, we focus on the retail industry field rather than on the manufacturing industry field where AI technology has already been widely introduced. In the retail industry, understanding customer interest in products is one of the significant issues, and Point-of-Sales (POS) data have been used for the analysis of customer data. However, the information of customers before the purchase cannot be obtained from the POS data, which was a limitation due to the characteristics of the data. To provide a solution to the problem, we propose a new customer interest estimation method using visual information obtained from a security camera. The proposed method consists of three phases: Re-identification phase, 3D posture estimation phase, and interest estimation phase. The advantage of our architecture is that the Reidentification phase enables individual identification of multiple persons, and the 3D posture estimation phase obtains highly expressive posture information. Then interest estimation phase is used to enable individual interest estimation. Finally, we achieve a high-level customer interest estimation performance using the data obtained from a real store.

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Section: Introductionmentioning

confidence: 99%