New Techniques of Foreground Detection, Segmentation and Density Estimation for Crowded Objects Motion Analysis

• Item type: Journal(1)
• 公開日: 2011-04-15
• タイトル: New Techniques of Foreground Detection, Segmentation and Density Estimation for Crowded Objects Motion Analysis
• タイトル / 言語: en
• タイトル: New Techniques of Foreground Detection, Segmentation and Density Estimation for Crowded Objects Motion Analysis
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 一般論文
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: School of Information Science and Engineering, Shandong University, China
• 著者所属: School of Information Science and Engineering, Shandong University, China
• 著者所属: Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
• 著者所属(英): en
• 著者所属(英): School of Information Science and Engineering, Shandong University, China
• 著者所属(英): en
• 著者所属(英): School of Information Science and Engineering, Shandong University, China
• 著者所属(英): en
• 著者所属(英): Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
• 著者名: Wei, Li; Xiaojuan, Wu; Hua-AnZhao
• 著者名(英): Wei, Li; Xiaojuan, Wu; Hua-An, Zhao
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Now video surveillance systems are being widely used, the capability of extracting moving objects and estimating moving object density from video sequences is indispensable for these systems. This paper proposes some new techniques of crowded objects motion analysis (COMA) to deal with crowded objects scenes, which consist of three parts: background removal, foreground segmentation, and crowded objects density estimation. To obtain optimal foregrounds, a combination approach of Lucas-Kanade optical flow and Gaussian background subtraction is proposed. For foreground segmentation, we put forward an optical flow clustering approach, which segments different crowded object flows, and then a block absorption approach to deal with the small blocks produced during clustering. Finally, we extract a set of 15 features from the foreground flows and estimate the density of each foreground flow. We employ self organizing maps to reduce the dimensions of the feature vector and to be a final classifier. Some experimental results prove that the proposed technique is useful and efficient.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Now video surveillance systems are being widely used, the capability of extracting moving objects and estimating moving object density from video sequences is indispensable for these systems. This paper proposes some new techniques of crowded objects motion analysis (COMA) to deal with crowded objects scenes, which consist of three parts: background removal, foreground segmentation, and crowded objects density estimation. To obtain optimal foregrounds, a combination approach of Lucas-Kanade optical flow and Gaussian background subtraction is proposed. For foreground segmentation, we put forward an optical flow clustering approach, which segments different crowded object flows, and then a block absorption approach to deal with the small blocks produced during clustering. Finally, we extract a set of 15 features from the foreground flows and estimate the density of each foreground flow. We employ self organizing maps to reduce the dimensions of the feature vector and to be a final classifier. Some experimental results prove that the proposed technique is useful and efficient.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 52, 号 4, p. 1820-1830, 発行日 2011-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764