文章基本信息

标题：A Novel Probabilistic Based Image Segmentation Model for Realtime Human Activity Detection
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作者：D.Ratna kishore ; M. Chandra Mohan ; Akepogu. Ananda Rao 等
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2016
卷号：7
期号：6
页码：11
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Automatic human activity detection is one of the difficult tasks in image segmentation application due tovariations in size, type, shape and location of objects. In the traditional probabilistic graphicalsegmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also,both directed and undirected graphical models such as Markov model, conditional random field havelimitations towards the human activity prediction and heterogeneous relationships. In this paper, we havestudied and proposed a natural solution for automatic human activity segmentation using the enhancedprobabilistic chain graphical model. This system has three main phases, namely activity pre-processing,iterative threshold based image enhancement and chain graph segmentation algorithm. Experimentalresults show that proposed system efficiently detects the human activities at different levels of the actiondatasets.
关键词：Human activity detection; Graphical models; Markov model; probability density distribution