Context-based Indoor Object Detection

基于上下文的室内物体检测

• 批准号: 0957016
• 负责人: YingLi Tian
• 金额: $ 10.17万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0957016&HistoricalAwards=false
• 关键词: Context; based; Indoor; Object; Detection

Robust and efficient indoor object detection can help people with severe vision impairment to independently access unfamiliar indoor environments. However, most existing object detection methods are developed either for a specific type of object (e.g. face) or for general nature objects (e.g., building, sky, etc) which cannot be directly applied to indoor objects due to following challenges: 1) big inter-class variations of the object model among different indoor environments, 2) small intra-class variations of different object models, 3) less texture compared to objects in natural scene or outdoor environments, 4) only part of the object is captured due to occlusions or blind user, 5) view and scale variations of the objects caused by the position and distance change between the user and the object, and 6) lack of suitable databases.This EAGER project is to explore new methods to detect indoor objects by incorporating the context information from signs (both text and iconic) and other visual clues such as signage of bathrooms and elevator floor numbers. The research enriches the study of object detection by incorporating context information, and leads to significant improvements over existing methods. The methods developed in this project provide new strategies and technologies for the blind and visually impaired to access unfamiliar indoor environments. The research also benefits many important research areas including video surveillance, intelligent conference rooms, video indexing, and human-computer interactions.

强大而高效的室内物体检测可以帮助患有严重视力障碍的人独立地进入不熟悉的室内环境。然而，大多数现有的对象检测方法是针对特定类型的对象（例如，面部）或针对一般自然对象（例如，建筑物、天空等），由于以下挑战而不能直接应用于室内对象：1）不同室内环境中对象模型的大类间变化，2）不同对象模型的小类内变化，3）与自然场景或室外环境中的对象相比纹理较少，4）由于遮挡或盲人用户，仅捕获对象的一部分，5）由于用户和物体之间的位置和距离变化而导致的物体的视图和比例变化，以及6）缺乏合适的数据库。EAGER项目旨在探索通过结合来自标志（文本和图标）的上下文信息和其他视觉线索（如浴室和电梯楼层号码）来检测室内物体的新方法。该研究通过引入上下文信息丰富了目标检测的研究，并导致了对现有方法的显着改进。该项目开发的方法为盲人和视障人士进入不熟悉的室内环境提供了新的策略和技术。该研究还惠及许多重要的研究领域，包括视频监控、智能会议室、视频索引和人机交互。