Visual search in real world: The role of dynamic and static optical information
Visual search in real world: The role of dynamic and static optical information
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DOI：
10.3724/sp.j.1042.2020.01219
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Jingning Pan;Huiyuan Zhang;Donghao Chen;Hongge Xu
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文献类型：
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作者：
Jingning Pan;Huiyuan Zhang;Donghao Chen;Hongge Xu
视觉搜索是一项无处不在的任务，也是人和动物的一项关键技能。现有的视觉搜索研究主要集中在注意引导和自上而下的认知对搜索效果的影响上。对视觉搜索的自下而上的影响，相当粗略地被简化为对象图像的显著程度。然而，当在现实世界中搜索时，当观察者和/或对象移动时，静态图像信息(其显著程度已在现有搜索模型中被考虑)和动态光流信息都是可用的。光流是由观察者和世界对象之间的相对运动产生的。因此，通过检测流动模式，观察者可以了解事件(定义为运动中的对象)的运动学属性，从而感知组成对象的物理属性，如质量、大小和摩擦系数等。这些物理属性区分物体，并允许观察者搜索特定的物体。我们将动态感知信息(即光流)集成到现有的搜索模型中，在两个研究中，我们测试了当观察者静止或移动时，动态和静态感知信息结合在一起对三维物体和运动的人的视觉搜索的影响。此外，我们试图开发一种训练协议来提高现实世界中的搜索效率。本项目的研究成果将为理解现实世界中的视觉搜索提供新的理论，并在人才培养和智能搜索设计方面有直接的应用。
Visual search is a ubiquitous task and a critical skill for men and animals. Existing studies on visual search mainly focus on attentional guidance and the top-down cognitive influences on search effectiveness. The bottom-up influence on visual search is, rather crudely, simplified as objects’ image saliency. However, when searching in real world, where the observer and/or objects move, both static image information (the saliency of which has been considered in existing search models) and dynamic optic flow information are available. Optic flow is generated by the relative motions between an observer and world objects. So by detecting flow patterns, observers get to know the kinematic properties of events (which is defined as objects in motion) and hence perceive the physical properties of constituent objects, such as the mass, size and frictional coefficient etc.. These physical properties distinguish objects and allow the observer to search for a particular one. We integrate dynamical perceptual information (i.e. optic flow) into existing search models and in two studies, we test how combined dynamical and static perceptional information affect visual search for three-dimensional objects and for moving people, when the observer is stationary or moving. Furthermore, we attempt to develop a training protocol that improves search effectiveness in real world. Findings from this project will bring forth new theories for understanding visual search in real world, and have direct applications on personnel training and intelligent search designs.