RI: Parameter-Sensitive and Dynamics-Aware Methods for Object Detection, Pose Estimation, and Tracking

RI：用于对象检测、姿态估计和跟踪的参数敏感和动态感知方法

• 批准号: 0713168
• 负责人: Stan Sclaroff
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713168&HistoricalAwards=false
• 关键词: RI; Parameter; Sensitive; Dynamics; Aware

Parameter-Sensitive and Dynamics-Aware Methods for Object Detection, Pose Estimation, and TrackingThe main goal of the proposed effort is to develop algorithms for simultaneous detection, parameter estimation, tracking, and classification of objects that exhibit high variability. While the aim is to develop a general framework time-varying objects, particular emphasis will be placed on modeling of articulated objects, like the human hand and human body. This research will focus on: (1) methods for dimensionality reduction that incorporate knowledge of object dynamics, (2) models that combine a collection of simpler local models to efficiently and accurately approximate nonlinear motion dynamics in a state-based model for tracking,(3) algorithms that can detect an instance of the object class in the image, and at the same time estimate the object's parameters.The resulting algorithms will be deployed in a prototype system that will support detection, parameter estimation, tracking and motion classification for video sequences of human motion. The methods will be tested with various motion capture and real-world video datasets of human motion: fullbody motion and gait, surveillance video, gestural communication, sports video, etc. Synthetic sequences, generated via computer graphics rendering from motion capture data, will be used in quantitative experiments where ground truth is required.The methods developed in this project would enable numerous applications that are valuable to society, for instance: homeland security; video- based analysis of human biomechanics for occupational safety, as well as dance and sports training; archive management and analysis for news, entertainment, and sports video; and non-intrusive monitoring of the motion patterns of handicapped, infirm, or elderly people to detect decline, danger, or to alert caregivers when needed.Keywords: Computer vision; object detection; articulated tracking; human motion analysisURL: http://www.cs.bu.edu/groups/ivc/ParameterSensitive/

参数敏感和动态感知方法的对象检测，姿态估计和跟踪所提出的努力的主要目标是开发算法，同时检测，参数估计，跟踪和分类的对象，表现出高的变化。 虽然目标是开发一个通用的框架随时间变化的对象，特别强调将放在建模的关节对象，如人手和人体。这项研究将侧重于：（1）结合了对象动态知识的降维方法，（2）将更简单的局部模型的集合联合收割机组合以有效且准确地近似基于状态的模型中的非线性运动动态以进行跟踪的模型，（3）可以检测图像中的对象类的实例的算法，并在同一时间估计的对象的参数。由此产生的算法将部署在一个原型系统，将支持检测，参数估计，跟踪和运动分类的视频序列的人体运动。 这些方法将使用各种运动捕捉和真实世界的人体运动视频数据集进行测试：全身运动和步态，监控视频，手势通信，体育视频等。通过计算机图形渲染从运动捕捉数据生成的合成序列，将用于需要地面实况的定量实验。该项目中开发的方法将实现对社会有价值的众多应用，例如：国土安全部基于视频的人体生物力学分析，用于职业安全，以及舞蹈和体育训练;新闻，娱乐和体育视频的档案管理和分析;以及残疾人，体弱者或老年人的运动模式的非侵入式监控，以检测衰退，危险或在需要时提醒护理人员。http://www.cs.bu.edu/groups/ivc/ParameterSensitive/