| dc.contributor.advisor | 廖文宏 | zh_TW |
| dc.contributor.advisor | Liao, Wen Hung | en_US |
| dc.contributor.author (作者) | 黃靈威 | zh_TW |
| dc.contributor.author (作者) | Huang, Ling Wei | en_US |
| dc.creator (作者) | 黃靈威 | zh_TW |
| dc.creator (作者) | Huang, Ling Wei | en_US |
| dc.date (日期) | 2008 | en_US |
| dc.date.accessioned | 8-十二月-2010 12:06:40 (UTC+8) | - |
| dc.date.available | 8-十二月-2010 12:06:40 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-十二月-2010 12:06:40 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0096971006 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/49471 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 96971006 | zh_TW |
| dc.description (描述) | 97 | zh_TW |
| dc.description.abstract (摘要) | 行人偵測為物件偵測領域中一個極具挑戰性的議題。其主要問題在於人體姿勢以及衣著服飾的多變性，加之以光源照射狀況迥異，大幅增加了辨識的困難度。吾人在本論文中提出利用共變異矩陣描述子及結合單純貝氏分類器與級聯支持向量機的線上學習辨識器，以增進行人辨識之正確率與重現率。實驗結果顯示，本論文所提出之線上學習策略在某些辨識狀況較差之資料集中能有效提升正確率與重現率達百分之十四。此外，即便於相同之初始訓練條件下，在USC Pedestrian Detection Test Set、 INRIA Person dataset 及 Penn-Fudan Database for Pedestrian Detection and Segmentation三個資料集中，本研究之正確率與重現率亦較HOG搭配AdaBoost之行人辨識方式為優。 | zh_TW |
| dc.description.abstract (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 thesis, we employ covariance feature and propose an on-line learning classifier which combines naïve Bayes classifier and cascade support vector machine (SVM) to improve the precision and recall rate of pedestrian detection in a still image.Experimental results show that our on-line learning strategy can improve precision and recall rate about 14% 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. | en_US |
| dc.description.tableofcontents | 第一章 研究目的 1第二章 簡介與文獻回顧 3第三章 共變異矩陣描述子 73.1 利用積分圖像計算任意矩形之共變異矩陣描述子 83.2 利用李群表示描述子間之運算 113.3 指數映射與對數映射 12第四章 在切空間中使用SVM 15第五章 行人辨識系統 205.1 行人辨識系統架構 205.2 訓練資料與測試資料來源及訓練方式 225.3 偵測視窗之整理合併 31第六章 實作、訓練與辨識結果 336.1 實作 336.2 訓練與辨識結果 376.3 與其他描述子及辨識器之比較 406.4 線上學習後辨識能力增減狀況 43第七章 結論與後續研究改進方向 56參考文獻 58 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0096971006 | en_US |
| dc.subject (關鍵詞) | 半監督式學習 | zh_TW |
| dc.subject (關鍵詞) | 支持向量機 | zh_TW |
| dc.subject (關鍵詞) | 單純貝氏分類器 | zh_TW |
| dc.subject (關鍵詞) | 共變異描述子 | zh_TW |
| dc.subject (關鍵詞) | Semi-supervised learning | en_US |
| dc.subject (關鍵詞) | Support vector machine | en_US |
| dc.subject (關鍵詞) | Naïve Bayes classifier | en_US |
| dc.subject (關鍵詞) | Covariance descriptor | en_US |
| dc.title (題名) | 應用共變異矩陣描述子及半監督式學習於行人偵測 | zh_TW |
| dc.title (題名) | Semi-supervised learning for pedestrian detection with covariance matrix feature | en_US |
| dc.type (資料類型) | thesis | en |
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