Reducing False Positive Rate for Viola-Jones Method in Hand Detection

Material type: 文書・図像類

Author: ブ, シュクケイほか

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Publication date: 2013-09

Material Format: Paper

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Note (General)：: The Viola-Jones object detector is a machine learning based method which combines Haar-like features with AdaBoost classifier. It is known that this m...

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Paper

Material Type: 文書・図像類
Title: Reducing False Positive Rate for Viola-Jones Method in Hand Detection
Author/Editor: ブ, シュクケイ
Bu, Shukukei
長橋, 宏
NAGAHASHI, HIROSHI
Author Heading: ブ, シュクケイ
Bu, Shukukei
長橋, 宏
NAGAHASHI, HIROSHI
Publication Date: 2013-09
Publication Date (W3CDTF): 2013-09
Periodical title: Technical Report of Technical Committee on Pattern Recognition
No. or year of volume/issue: 113 no. 196
Volume: 113
Issue: no. 196
Pages: 149-153
Text Language Code: eng
Target Audience: 一般
Note (General): The Viola-Jones object detector is a machine learning based method which combines Haar-like features with AdaBoost classifier. It is known that this method is the mainstream in face detections due to its high performance of both detection speed and precision. However, the false positive rate increases dramatically when this approach is applied to detect hands in a sign language video which includes other skin color parts such as faces or arms. In this paper, we analyze the reason for mis-detecting of these points and propose a new method to reduce the false positive rate. Experimental results have shown that our method is more robust to hand motion and decreases the false positive rate significantly.
identifier:oai:t2r2.star.titech.ac.jp:50279297
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 東京工業大学 : 東京工業大学リサーチリポジトリ