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

目标检测、位姿估计和跟踪的参数敏感和动态感知方法研究的主要目标是开发同时检测、参数估计、跟踪和分类具有高度变异性的目标的算法。虽然目标是开发一个随时间变化的对象的一般框架，但重点将特别放在关节对象的建模上，如人手和人体。本研究将集中于：(1)结合目标动力学知识的降维方法；(2)结合更简单的局部模型集合的模型来高效而准确地逼近基于状态的跟踪模型中的非线性运动动力学；(3)能够检测图像中目标类别的实例并同时估计目标参数的算法；所得到的算法将被部署在一个原型系统中，该系统将支持对人体运动视频序列的检测、参数估计、跟踪和运动分类。这些方法将在人体运动的各种运动捕获和真实世界视频数据集上进行测试：全身运动和步态、监控视频、手势交流、运动视频等。由运动捕获数据通过计算机图形渲染生成的合成序列将用于需要真实情况的定量实验。该项目开发的方法将使许多对社会有价值的应用得以实现，例如：国土安全；基于视频的人体生物力学分析，用于职业安全，以及舞蹈和体育培训；新闻、娱乐和体育视频的档案管理和分析；对残疾人、体弱者或老年人的运动模式进行非侵入性监测，以检测衰退、危险或在需要时提醒照顾者。关键词：计算机视觉；对象检测；关节跟踪；人体运动分析URL：http://www.cs.bu.edu/groups/ivc/ParameterSensitive/

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/