NDLI: Deleted Interpolation Using a Hierarchical Bayesian Grammar Network for Recognizing Human Activity

Content Provider	IEEE Xplore Digital Library
Author	Kitani, K.M. Sato, Y. Sugimoto, A.
Copyright Year	2005
Description	Author affiliation: Institute of Industrial Science, The University of Tokyo, Tokyo 158-8505, Japan. kitani@iis.u-tokyo.ac.jp (Kitani, K.M.)
Abstract	From the viewpoint of an intelligent video surveillance system, the high-level recognition of human activity requires a priori hierarchical domain knowledge as well as a means of reasoning based on that knowledge. We approach the problem of human activity recognition based on the understanding that activities are hierarchical, temporally constrained and temporally overlapped. While stochastic grammars and graphical models have been widely used for the recognition of human activity, methods combining hierarchy and complex queries have been limited. We propose a new method of merging and implementing the advantages of both approaches to recognize activities in real-time. To address the hierarchical nature of human activity recognition, we implement a hierarchical Bayesian network (HBN) based on a stochastic context-free grammar (SCFG). The HBN is applied to digressive substrings of the current string of evidence via deleted interpolation (DI) to calculate the probability distribution of overlapped activities in the current string. Preliminary results from the analysis of activity sequences from a video surveillance camera show the validity of our approach.
Starting Page	239
Ending Page	246
File Size	3217299
Page Count	8
File Format	PDF
ISBN	0780394240
DOI	10.1109/VSPETS.2005.1570921
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Interpolation Graphical models Bayesian methods Merging Humans Stochastic processes Video surveillance Cameras Probability distribution Intelligent systems
Content Type	Text
Resource Type	Article

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