Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
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DOI：
10.1109/tcsvt.2008.2005612
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发表时间：
2008-11
影响因子：
8.4
通讯作者：
Zhongna Zhou;X. Chen;Y. Chung;Zhihai He;T. Han;J. Keller
中科院分区：
文献类型：
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作者：
Zhongna Zhou;X. Chen;Y. Chung;Zhihai He;T. Han;J. Keller
在这项工作中，我们研究如何将连续视频监控和智能视频处理应用于老年护理中，以帮助老年人独立生活，提高老年护理实践的效率。更具体地说，我们开发了一个用于老年护理视频监控的自动化活动分析和摘要。在目标层面，构建了一种先进的室内环境下的人体轮廓提取、人体检测和跟踪算法。在特征层，我们开发了一种自适应学习方法，在没有标定的情况下，从单个摄像机视角估计人的物理位置和运动速度。在动作层面，我们探索了用于人类动作识别的分层决策树和降维方法。我们提取重要的日常生活活动(ADL)统计数据，用于自动功能评估。为了测试和评估所提出的算法和方法，我们在真实的生活环境中部署了大约一个月的摄像头系统，并收集了超过200小时(超过600G字节)的活动监控视频。我们在这些海量视频数据集上的广泛测试表明，所提出的自动化活动分析系统是非常有效的。
In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.