期刊
IMAGE AND VISION COMPUTING
卷 20, 期 5-6, 页码 349-358出版社
ELSEVIER
DOI: 10.1016/S0262-8856(02)00007-0
关键词
gesture recognition; behavior recognition; hidden Markov models; condensation; motion-based recognition; temporal modelling
Human activities are characterised by the spatio-temporal structure of their motion patterns. Such structures can be represented as temporal trajectories in a high-dimensional feature space of closely correlated measurements of visual observations. Models of such temporal structures need to account for the probabilistic and uncertain nature of motion patterns, their non-linear temporal scaling and ambiguities in temporal segmentation. In this paper, we address such problems by introducing a statistical dynamic framework to model and recognise human activities based on learning prior and continuous propagation of density models of behaviour patterns. Prior is learned from example sequences using hidden Markov models and density models are augmented by current visual observations. (C) 2002 Elsevier Science B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据