Mining and Indexing Spatio-Temporal Patterns in Video Databases of Human Motion

人体运动视频数据库中的时空模式挖掘和索引

• 批准号: 0308213
• 负责人: Stan Sclaroff
• 金额: $ 40.5万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0308213&HistoricalAwards=false
• 关键词: Mining; Indexing; Spatio; Temporal; Patterns

The aim of this research project is to develop and test methods for indexing, retrieval, and data mining of human motion trajectories in video databases. Computer vision techniques are being devised for automatic extraction of human motion time series data from video. Data mining algorithms are being developed that can be used to discover clusters and other patterns in the extracted motion time-series data. One promising direction being explored is to model the observed motion time series sequences with a finite mixture of Hidden Markov Models (HMMs). Use of the HMM representation presents certain advantages with regard to modeling; however, it presents important challenges for the design of efficient clustering, indexing, and retrieval algorithms. Thus more efficient, sampling-based and embedding-based methods must be formulated. The resulting ideas are evaluated in a prototype video retrieval system, with real-world video datasets that depict human body motion. Synthetic sequences (e.g., generated via computer graphics) are used in quantitative performance experiments where ground truth information is required. The products of this research effort can enable numerous applications that are valuable to society: 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 video database management for non-intrusive monitoring of the motion patterns of handicapped, infirm, or elderly people to detect decline, danger, and to alert caregivers when needed. Results can be accessed at the project's Web site (http://www.cs.bu.edu/groups/ivc/MotionMining/).

该研究项目的目的是开发和测试视频数据库中人体运动轨迹的索引、检索和数据挖掘方法。计算机视觉技术正在被设计用于从视频中自动提取人体运动时间序列数据。数据挖掘算法正在开发中，可用于发现提取的运动时间序列数据中的簇和其他模式。正在探索的一个有希望的方向是使用隐马尔可夫模型（HMM）的有限混合来对观察到的运动时间序列序列进行建模。使用 HMM 表示在建模方面具有一定的优势；然而，它给高效聚类、索引和检索算法的设计带来了重要挑战。因此，必须制定更有效的、基于采样和基于嵌入的方法。由此产生的想法在原型视频检索系统中进行评估，并使用描述人体运动的真实视频数据集。合成序列（例如，通过计算机图形生成）用于需要真实信息的定量性能实验。这项研究工作的产品可以实现许多对社会有价值的应用：国土安全；基于视频的人体生物力学分析，以确保职业安全以及舞蹈和运动训练；新闻、娱乐和体育视频的档案管理和分析；视频数据库管理，用于对残疾人、体弱者或老年人的运动模式进行非侵入式监控，以检测身体衰弱、危险，并在需要时向护理人员发出警报。结果可以在该项目的网站 (http://www.cs.bu.edu/groups/ivc/MotionMining/) 上获取。