RI-Small: Optimal Automated Design of Cascaded Object Detectors

RI-Small：级联物体检测器的优化自动化设计

• 批准号: 0812235
• 负责人: Nuno Vasconcelos
• 金额: $ 33.7万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812235&HistoricalAwards=false
• 关键词: RI; Small; Optimal; Automated; Design

Object detection cascades are one of the most significant recent developments in computer vision. By enabling real-time object detection, they are a potentially disruptive technology, which is already making a commercial impact in industries as diverse as digital photography, automotive, surveillance, personal identification, and traffic safety, among others. However, this disruptive potential is currently stifled by the substantial complexity of training detector cascades. In practice, this complexity limits the application of the cascaded architecture to a small set of domains (most notably face detection) which have been heavily researched by the academic community and for which detectors are publicly available. This project aims to eliminate the complexity hurdle, by laying the theoretical and algorithmic foundations for the fully-automated, low-complexity, design of optimal detection cascades, which guarantee high detection-rate while minimizing false-positive rate and detection complexity. In particular, the project addresses major current roadblocks in architecture design, detector design, and training complexity, through novel contributions in cost sensitive boosting, weak learners, and optimal cascade design algorithms. All contributions will be evaluated in the context of an effort to deploy real-time animal detectors in some of the most popular wild-life attractions of San Diego. This also provides an exciting and unusual opportunity for the involvement of undergraduates in research.More information on the project can be found at http://www.svcl.ucsd.edu

目标检测级联是计算机视觉领域最近最重要的发展之一。通过实现实时目标检测，它们是一种潜在的颠覆性技术，已经在数字摄影、汽车、监控、个人识别和交通安全等多个行业产生了商业影响。然而，这种破坏性的潜力目前被训练检测器级联的巨大复杂性所抑制。在实践中，这种复杂性将级联架构的应用限制在一小部分领域（最明显的是人脸检测），这些领域已经被学术界大量研究，并且检测器是公开可用的。本项目旨在消除复杂性障碍，为全自动、低复杂度的最优检测级联设计奠定理论和算法基础，在保证高检测率的同时最大限度地降低误报率和检测复杂度。特别是，该项目通过在成本敏感增强、弱学习器和最优级联设计算法方面的新贡献，解决了当前架构设计、检测器设计和训练复杂性方面的主要障碍。所有贡献都将在圣地亚哥一些最受欢迎的野生动物景点部署实时动物探测器的努力背景下进行评估。这也为本科生参与研究提供了一个令人兴奋和不同寻常的机会。有关该项目的更多信息可在http://www.svcl.ucsd.edu上找到