A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909533682532352 |
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| author | Rukundo, Shimmy Wang, David Wongnonthawitthaya, Front Sidibé, Youssouf Kim, Minsik Su, Emily Zhang, Jiale |
| author_facet | Rukundo, Shimmy Wang, David Wongnonthawitthaya, Front Sidibé, Youssouf Kim, Minsik Su, Emily Zhang, Jiale |
| contents | Autonomous stores leverage advanced sensing technologies to enable cashier-less shopping, real-time inventory tracking, and seamless customer interactions. However, these systems face significant challenges, including occlusion in vision-based tracking, scalability of sensor deployment, theft prevention, and real-time data processing. To address these issues, researchers have explored multi-modal sensing approaches, integrating computer vision, RFID, weight sensing, vibration-based detection, and LiDAR to enhance accuracy and efficiency. This survey provides a comprehensive review of sensing technologies used in autonomous retail environments, highlighting their strengths, limitations, and integration strategies. We categorize existing solutions across inventory tracking, environmental monitoring, people-tracking, and theft detection, discussing key challenges and emerging trends. Finally, we outline future directions for scalable, cost-efficient, and privacy-conscious autonomous store systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems Rukundo, Shimmy Wang, David Wongnonthawitthaya, Front Sidibé, Youssouf Kim, Minsik Su, Emily Zhang, Jiale Signal Processing Systems and Control Image and Video Processing J.0; J.7; A.1 Autonomous stores leverage advanced sensing technologies to enable cashier-less shopping, real-time inventory tracking, and seamless customer interactions. However, these systems face significant challenges, including occlusion in vision-based tracking, scalability of sensor deployment, theft prevention, and real-time data processing. To address these issues, researchers have explored multi-modal sensing approaches, integrating computer vision, RFID, weight sensing, vibration-based detection, and LiDAR to enhance accuracy and efficiency. This survey provides a comprehensive review of sensing technologies used in autonomous retail environments, highlighting their strengths, limitations, and integration strategies. We categorize existing solutions across inventory tracking, environmental monitoring, people-tracking, and theft detection, discussing key challenges and emerging trends. Finally, we outline future directions for scalable, cost-efficient, and privacy-conscious autonomous store systems. |
| title | A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems |
| topic | Signal Processing Systems and Control Image and Video Processing J.0; J.7; A.1 |
| url | https://arxiv.org/abs/2503.07997 |