Article View/Open
Publication Export
Related Publications in TAIR
- > Simple Record
- > Full Record
| Field |
Value |
| Title: | Pedestrian detection using covariance descriptor and on-line learning |
| Authors: | Liao, Wen-Hung;Huang, Ling-Wei
廖文宏 |
| Contributors: | 資科系 |
| Keywords: | 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 |
| Date: | 2011-11 |
| Issue Date: | 2015-04-08 17:34:07 (UTC+8) |
| Abstract: | 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. |
| Relation: | Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, 論文編號 6120740, 179-182
10.1109/TAAI.2011.38 |
| Data Type: | conference |
| DOI: | http://dx.doi.org/10.1109/TAAI.2011.38 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 資科系 | |
| dc.creator (Authors) | Liao, Wen-Hung;Huang, Ling-Wei | |
| dc.creator (Authors) | 廖文宏 | zh_TW |
| dc.date (Date) | 2011-11 | |
| dc.date.accessioned | 2015-04-08 17:34:07 (UTC+8) | - |
| dc.date.available | 2015-04-08 17:34:07 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2015-04-08 17:34:07 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74408 | - |
| dc.description.abstract (Abstract) | 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. | |
| dc.format.extent | 281 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (Relation) | Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, 論文編號 6120740, 179-182 | |
| dc.relation (Relation) | 10.1109/TAAI.2011.38 | |
| dc.subject (Keywords) | 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 | |
| dc.title (Title) | Pedestrian detection using covariance descriptor and on-line learning | |
| dc.type (Data Type) | conference | en |
| dc.identifier.doi (DOI) | 10.1109/TAAI.2011.38 | en_US |
| dc.doi.uri | http://dx.doi.org/10.1109/TAAI.2011.38 | en_US |
(644)