Detection of Abandoned Luggage and Owner Tracking at Sensitive Public Areas

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/01/01

タイトル(英語): Detection of Abandoned Luggage and Owner Tracking at Sensitive Public Areas

著者名: Stephen Karungaru (Graduate School of Advanced Technology and Science The University of Tokushima), Kenji Terada (Graduate School of Advanced Technology and Science The University of Tokushima), Minoru Fukumi (Graduate School of Advanced Technology and Sc

著者名(英語): Stephen Karungaru (Graduate School of Advanced Technology and Science The University of Tokushima), Kenji Terada (Graduate School of Advanced Technology and Science The University of Tokushima), Minoru Fukumi (Graduate School of Advanced Technology and Science The University of Tokushima)

キーワード: Luggage owner，Active background，abandoned objects，Earth Mover's Distance，Discriminant Analysis

要約(英語): Abandoned objects in sensitive congested public areas like airports or train stations pose a major security threat. Therefore, in this paper, to solve the problem, we propose a novel method for the detection of abandoned luggage, tracking its owner and extracting any other necessary information using multi-threshold pixel based dynamic background model and Earth Mover's Distance (EMD) signature matching. The public area selected for experiments is a train station. The background model is created by learning the color variance in all pixels by allowing multiple thresholds per pixel. After learning, pruning unnecessary thresholds improves the foreground extraction speed. Blob noise and outliers including shadows are deleted by a binarization method based on the Discriminant Analysis (DA) method. A color signature created using the HSV color space that is fast to process, is matched using the EMD metric to track blobs. Once an abandoned object candidate is found, a slower but more accurate SURF algorithm is used to extract feature points for further tracking. Stationary objects after this phase are considered to be abandoned luggage. To prove the effectiveness of the proposed method, experiments are conducted using the i-Lids dataset (2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007)) achieving a frame-based average accuracy of about 93%.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.1 （2013） 特集：2012 Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2012)

本誌掲載ページ: 67-73 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/1/133_67/_article/-char/ja/