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

摘要：PI最近开发了一种基于区分训练的可变形部分的模型，用于检测图像中的目标。在2006年的帕斯卡人检测挑战中，该模型的平均精度比最佳性能提高了两倍。我们在2007年的物体检测挑战赛中赢得了六个班级的冠军。自2007年挑战以来的修改提高了我们的成绩，现在我们在包括Person类在内的20个班级中有10个班级的成绩超过了2007年最好的挑战赛成绩。这些成果依赖于一种新发展的潜在变量模型的判别训练方法，我们称之为潜在支持向量机(LSVM)。我们建议以多种方式推广这一方法。研究将集中在更深层次的潜在信息上，如子分类(混合模型)、三维姿势和图形/背景分割。它们还将使用类层次结构、可视化单词以及带有部件和子部件的分层对象模型。我们还提出了一种使用支持向量机训练模型的通用方法，例如基于几何的三维模型，这些模型的参数是高度非线性的。这项研究的所有方面都与经验性能密切相关--除非它实际改进了最先进的技术，否则不会采用任何方法。我们的目标一直是，也将继续是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水平。这个项目的进展可以在http：//ttic.uchicago.edu/~dmcallester/objects.html上找到

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