Human Postures Recognition by Self-learning Adaptive Fuzzy-Rule Based Classifier System
Date Issued
2007
Date
2007
Author(s)
Feng, Hsiao-Tien
DOI
en-US
Abstract
Intelligent video surveillance system is discussed for years and applied in many areas, like home care systems, security in the public place and so on. Due to this technique development, it can lower down the product cost and decrease the error judgment by human.
In this thesis, we will focus on the home care systems, it means that we hope to establish a system can analysis the human activities automatically. As the growth of the elderly, more and more people can not take care of their parents or the elderly all the time. It manes that we want to build a system have the ability to watch the elderly and alarm the people when they have some abnormal behavior, like falling down. In our concept we first want to recognize the human postures, so we set a CCD camera to grab the image contained the elderly and environment. And then we separate the people from the environment using the background subtraction method. After the object is subtracted, some parameters from the silhouette will be extracted and viewed as the input of our classified system. As for the classified system, we use the adaptive fuzzy rule-based classified system. It can generate the fuzzy rules automatically according to the feature parameters we extract, and give a good performance in classifying the postures.
Subjects
居家看護系統
人體姿態辨識
乏析規則
背景去除
human activities
background subtraction
adaptive fuzzy rule based classified system
Type
thesis
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