政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/74408

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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/74408

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题名:	Pedestrian detection using covariance descriptor and on-line learning
作者:	Liao, Wen-Hung;Huang, Ling-Wei 廖文宏
贡献者:	資科系
关键词:	Bayes Classifier;Covariance descriptor;Covariance features;Data sets;Flexible bodies;Object classification;Online learning;Pedestrian detection;Precision and recall;Still images;Test sets;Training conditions;Adaptive boosting;Artificial intelligence;Statistical tests;Support vector machines;E-learning
日期:	2011-11
上传时间:	2015-04-08 17:34:07 (UTC+8)
摘要:	Pedestrian detection is an important yet challenging problem in object classification due to flexible body pose, loose clothing and ever-changing illumination. In this paper, we employ covariance features and propose an on-line learning classifier which combines naïve Bayes classifier and cascade support vector machines (SVM) to improve the precision and recall rate of pedestrian detection in still images. Experimental results show that our strategy can significantly increase both precision and recall rates in some difficult situations. Furthermore, even under the same initial training condition, our method outperforms HOG + AdaBoost in USC Pedestrian Detection Test Set, INRIA Person dataset and Penn-Fudan Database for Pedestrian Detection and Segmentation. © 2011 IEEE.
關聯:	Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, 論文編號 6120740, 179-182 10.1109/TAAI.2011.38
数据类型:	conference
DOI 連結:	http://dx.doi.org/10.1109/TAAI.2011.38
DOI:	10.1109/TAAI.2011.38
显示于类别:	[資訊科學系] 會議論文

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