RI-Small: Collaborative Research: Discriminative Latent Variable Object Detection

RI-Small：协作研究：判别性潜变量目标检测

• 批准号: 0812428
• 负责人: Deva Ramanan
• 金额: --
• 依托单位: University of California-Irvine
• 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: 2008-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812428&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Discriminative

Abstract:The PIs have recently developed a discriminatively trained deformable part based model for detecting objects in images. This model achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. Our entry in the 2007 object detection challenge won in six classes. Modifications since the 2007 challenge have improved our results so that we now outperform the best 2007 challenge results in ten of twenty classes including the person class. These accomplishments rely on a newly developed approach to discriminative training for latent variable models which we call a latent SVM (LSVM). We propose to extend this methodology in a variety of ways. The research will focus on deeper latent information such as subclassification (mixture models), three dimensional pose, and figure/ground segmentations. They will also use class hierarchies, visual words, and hierarchical object models with parts and sub-parts. We also propose a general methodology for using SVM training to train models, such as geometry-based 3D models, which are highly nonlinear in model parameters. All aspects of this research are strongly tied to empirical performance -- no method will be adopted unless it actually improves the state of the art. The goal has been, and will continue to be, to improve the state of the art through the use of semantically deeper models and improved general purpose machine learning methods.Progress on this project can be found at http:/ttic.uchicago.edu/ ~dmcallester/objects.html

摘要：PI最近开发了一种基于歧视的基于可变形的零件模型，用于检测图像中的对象。与2006年帕斯卡人检测挑战中最佳性能相比，该模型的平均精度取得了两倍的提高。我们在2007年对象检测挑战中的参赛作品赢得了六堂课。自2007年挑战以来的修改改善了我们的结果，因此我们现在在包括人类班级在内的20个课程中的十个班级中优于2007年最佳挑战结果。 这些成就依赖于一种新开发的方法来对潜在变量模型进行判别培训，我们称之为潜在的SVM（LSVM）。 我们建议以多种方式扩展这种方法。该研究将集中于更深的潜在信息，例如亚分类（混合模型），三维姿势和图/地面分割。他们还将使用零件和子部分的类层次结构，视觉单词和分层对象模型。我们还提出了一种通用方法，用于使用SVM训练来训练模型，例如基于几何的3D模型，该模型在模型参数中是高度非线性的。这项研究的所有方面都与经验绩效密切相关 - 除非它实际改善了艺术的状态，否则不会采用任何方法。通过使用语义更深的模型和改进的通用机器学习方法来改善最新技术的目标是，并且将继续存在。