Human Behavior Analysis Using Multiple Features and AdaBoosting
碩士 === 國立中正大學 === 資訊工程所 === 96 === Most human behavior systems nowadays use single feature or single classifier algorithm to do human behavior analysis. But if we use only one feature to analyze human behavior, many analysis can’t be do well in many case. For example, if we only use shape silhouette...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2007
|
Online Access: | http://ndltd.ncl.edu.tw/handle/41181196990445088723 |
id |
ndltd-TW-096CCU05392027 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-096CCU053920272015-10-13T11:31:38Z http://ndltd.ncl.edu.tw/handle/41181196990445088723 Human Behavior Analysis Using Multiple Features and AdaBoosting 使用多重特徵及AdaBoosting之人類行為分析 Wei-chiau Li 李偉僑 碩士 國立中正大學 資訊工程所 96 Most human behavior systems nowadays use single feature or single classifier algorithm to do human behavior analysis. But if we use only one feature to analyze human behavior, many analysis can’t be do well in many case. For example, if we only use shape silhouette information to do human behavior analysis, the change information between frames in continue time doesn’t be considered in analysis procedure. And we also can’t realize what is the best analysis algorithm in some specific environment. In this paper, we use AdaBoosting based algorithm, with multiple features and different classifier, to combine all of them, and to create more reliable analysis results. For using AdaBoosting, one the restriction is all classifier must be weak. In this paper, we unanalyzed some actions deliberately to create many weak classifiers from original classifier. Finally, we combine all of them using AdaBoosting. The accuracy rate of these classifier is much lower than general classifiers, but after combing them by AdaBoosting algorithm, we will find that the better analysis results can be get in the experiments. Jin-jang Leou 柳金章 2007 學位論文 ; thesis 54 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立中正大學 === 資訊工程所 === 96 === Most human behavior systems nowadays use single feature or single classifier algorithm to do human behavior analysis. But if we use only one feature to analyze human behavior, many analysis can’t be do well in many case. For example, if we only use shape silhouette information to do human behavior analysis, the change information between frames in continue time doesn’t be considered in analysis procedure. And we also can’t realize what is the best analysis algorithm in some specific environment. In this paper, we use AdaBoosting based algorithm, with multiple features and different classifier, to combine all of them, and to create more reliable analysis results.
For using AdaBoosting, one the restriction is all classifier must be weak. In this paper, we unanalyzed some actions deliberately to create many weak classifiers from original classifier. Finally, we combine all of them using AdaBoosting. The accuracy rate of these classifier is much lower than general classifiers, but after combing them by AdaBoosting algorithm, we will find that the better analysis results can be get in the experiments.
|
author2 |
Jin-jang Leou |
author_facet |
Jin-jang Leou Wei-chiau Li 李偉僑 |
author |
Wei-chiau Li 李偉僑 |
spellingShingle |
Wei-chiau Li 李偉僑 Human Behavior Analysis Using Multiple Features and AdaBoosting |
author_sort |
Wei-chiau Li |
title |
Human Behavior Analysis Using Multiple Features and AdaBoosting |
title_short |
Human Behavior Analysis Using Multiple Features and AdaBoosting |
title_full |
Human Behavior Analysis Using Multiple Features and AdaBoosting |
title_fullStr |
Human Behavior Analysis Using Multiple Features and AdaBoosting |
title_full_unstemmed |
Human Behavior Analysis Using Multiple Features and AdaBoosting |
title_sort |
human behavior analysis using multiple features and adaboosting |
publishDate |
2007 |
url |
http://ndltd.ncl.edu.tw/handle/41181196990445088723 |
work_keys_str_mv |
AT weichiauli humanbehavioranalysisusingmultiplefeaturesandadaboosting AT lǐwěiqiáo humanbehavioranalysisusingmultiplefeaturesandadaboosting AT weichiauli shǐyòngduōzhòngtèzhēngjíadaboostingzhīrénlèixíngwèifēnxī AT lǐwěiqiáo shǐyòngduōzhòngtèzhēngjíadaboostingzhīrénlèixíngwèifēnxī |
_version_ |
1716845579627134976 |