智能货架系统用于超市客户行为跟踪
John Anthony C Jose1, Christopher John B Bertumen1, Marianne Therese C Roque1
1Department of Electronics and Computer Engineering, Gokongwei College of Engineering, De La Salle University, 2401 Taft Avenue, Malate, Manila 1004, Metro Manila, Philippines.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一个智能货架系统,可以跟踪客户的互动,包括"跨位置"行为,以改善零售分析. 该系统实现了76%的召回率,提高了对现实世界购物模式的理解.
科学领域:
- 计算机视觉 计算机视觉
- 零售分析 零售分析
- 人与计算机的交互
背景情况:
- 销售点数据往往忽略了购买前的客户行为.
- 传统的零售分析忽略了"跨地点"的交互,客户偏离了直接的货架接触.
- 准确跟踪多人和多产品交互对于现实世界零售场景至关重要.
研究的目的:
- 开发和评估一个智能货架系统,能够跟踪客户与产品的互动,包括跨位置事件.
- 通过结合以前遗漏的客户行为来增强零售分析.
- 在动态购物环境中提高客户行为分析的准确性.
主要方法:
- 一个结合产品跟踪负载单元和客户跟踪摄像头 (PACK-RMPF) 的系统.
- 使用R-CNN和StrongSORT来处理时间序列视觉数据.
- 采用RANSAC建模和颗粒过,用于客户与产品的关联和轨迹预测.
- 使用NTP服务器同步重量和视觉子系统.
主要成果:
- 该系统显示,对于一般客户与产品的互动,平均召回率为76.33%.
- 实现了高于79%的召回率,特别是在跨位置的实例中.
- 成功地将客户关键点轨迹与产品位置相对应.
结论:
- 开发的智能货架系统有效地捕捉了复杂的客户与产品的互动,包括跨位置事件.
- 这种方法显著提高了零售分析数据的完整性和准确性.
- 这些发现强调了包括非传统交互对于全面了解购物者的行为的重要性.
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