-
- ZHAO Xinyue
- Graduate School of Information Science and Technology, Hokkaido University
-
- SATOH Yutaka
- National Institute of Advanced Industrial Science and Technology (AIST)
-
- TAKAUJI Hidenori
- Muroran Institute of Technology
-
- KANEKO Shun'ichi
- Graduate School of Information Science and Technology, Hokkaido University
この論文をさがす
抄録
This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.
収録刊行物
-
- IEICE Transactions on Information and Systems
-
IEICE Transactions on Information and Systems E95-D (2), 646-657, 2012
一般社団法人 電子情報通信学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390001204379099392
-
- NII論文ID
- 10030611332
-
- NII書誌ID
- AA10826272
-
- ISSN
- 17451361
- 09168532
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可