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首页> 外文期刊>Electronic Letters on Computer Vision and Image Analysis: ELCVIA >Multimodal Assessment of Shopping Behavior
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Multimodal Assessment of Shopping Behavior

机译：购物行为的多模式评估

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Automatic understanding and recognition of human shopping behavior has many potential applications, attracting an increasing interest in the marketing domain. A first behavior cue regards the human movement patterns, then for obtaining a better overview of what is happening inside an environment, context information is used. More information regarding behavior can be extracted, by analyzing the interaction patterns with objects in the environment. Finally, facial expressions, which can be used to assess a person's reaction to an object or in our case study to a product are employed as another informative behavior cue. Each intermediary analysis stream (trajectory analysis, action recognition, ROI detection module, and facial expression analysis), provides an input to the reasoning model, which based on the observables formulates a hypothesis regarding the most likely behavioral model. We integrated the different types of information on the semantic level, by implementing a multi-level framework. Finally, we evaluated this system in the ShopLab, in a real supermarket, and the product appreciation in a laboratory setting. The results show the feasibility of the approach in the recognition of trajectories (93%), shopping actions (91.6%), action units (93%), facial expressions (84%), and the most important behavioral types (87%).
机译：对人类购物行为的自动理解和识别具有许多潜在的应用，在市场营销领域引起了越来越多的兴趣。第一个行为提示涉及人类的运动模式，然后为了获得对环境内部正在发生的事件的更好的了解，使用了上下文信息。通过分析环境中与对象的交互模式，可以提取有关行为的更多信息。最后，面部表情可以用来评估人对物体的反应，或者在我们的案例中是对产品的反应，它是另一种有益的行为提示。每个中间分析流（轨迹分析，动作识别，ROI检测模块和面部表情分析）都为推理模型提供了输入，推理模型基于可观察对象提出了有关最可能的行为模型的假设。通过实现多级框架，我们在语义级别上集成了不同类型的信息。最后，我们在一家真正的超级市场的​​ShopLab中评估了该系统，并在实验室中对产品进行了评估。结果表明，该方法在识别轨迹（93％），购物动作（91.6％），动作单位（93％），面部表情（84％）和最重要的行为类型（87％）方面是可行的。

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