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

基本信息

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

项目摘要

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

对象检测级联反应是计算机视觉中最重要的发展之一。通过启用实时对象检测，它们是一种潜在的破坏性技术，它已经对数字摄影，汽车，监视，个人身份和交通安全等行业产生了商业影响。但是，目前，这种破坏性潜力被训练检测器级联反应的实质性复杂性所扼杀。实际上，这种复杂性将级联体系结构的应用限制在一系列的域（最著名的是面临检测），这些领域已由学术界进行了大量研究，并为其公开可用。该项目旨在通过为完全自动化的低复杂性，最佳检测级联设计的理论和算法基础奠定理论和算法基础，从而消除复杂性障碍，这是最佳检测级联反应的设计，这些级联级联的设计率很高，同时最大程度地减少了假阳性速率和检测复杂性。特别是，该项目通过对成本敏感的提升，弱学习者和最佳级联设计算法的新颖贡献来解决建筑设计，探测器设计和培训复杂性的主要障碍。所有贡献都将在努力将实时动物探测器部署在圣地亚哥一些最受欢迎的野生生命景点中进行评估。这也为本科生参与研究提供了一个令人兴奋的和不寻常的机会。该项目的更多信息可在http://www.svcl.ucsd.edu上找到。