摘要
针对老年人异常行为检测,分析老年人日常行为特点及规律,对其日常运动轨迹进行采样和量化,并使用基于改进的HMM模型进行轨迹行为习惯建模,结合模糊C均值聚类算法提取轨迹标志点确定HMM初值,并通过改进的重估公式和反馈滑动窗口进行训练与检测。实验结果表明,运动轨迹可以作为老年人行为习惯的一个重要描述形式,利用位置和轨迹对家庭环境下的异常行为检测是有效的,且能保证较高的准确率和较低的漏检率。
For abnormal behavior detection of the elderly,some characteristics and rules of daily behavior can be gotten from the daily positon and motion trajectory.Through the sampling and quantization of trajectory,an efficient algorithm is proposed for abnormal detection based on the imroved HMM model and feedback sliding window.The trajectory landmark points and HMM initial value with the fuzzy C-means clustering is identified and the improved formula revaluation is given,then training and testing with feedback sliding window is carried on.Comparative experiments show that the method is effective for the abnormal detection in daily with position and trajectory and can ensure higher accuracy and lower false negative rate.
出处
《计算机工程与设计》
CSCD
北大核心
2012年第2期735-739,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(60975062)
河北省自然科学基金项目(F2010001295)
关键词
行为检测
运动轨迹
滑动窗口
标志点
HMM
behavior detection
motion trajectory
sliding window
landmark points
HMM
