Efficient Acquisition of Human Existence Priors from Motion Trajectories

• Item type: Trans(1)
• 公開日: 2010-11-10
• タイトル: Efficient Acquisition of Human Existence Priors from Motion Trajectories
• タイトル / 言語: en
• タイトル: Efficient Acquisition of Human Existence Priors from Motion Trajectories
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Research Paper
• 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: Nara Institute of Science and Technology
• 著者所属: Nara Institute of Science and Technology
• 著者所属: Nara Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): Nara Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): Nara Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): Nara Institute of Science and Technology
• 著者名: Hitoshi, Habe; Hidehito, Nakagawa; Masatsugu, Kidode
• 著者名(英): Hitoshi, Habe; Hidehito, Nakagawa; Masatsugu, Kidode
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play an important role in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high prior probability is assigned to an area having the same color as past human trajectories. We use a particle filter for representing and updating the prior probability. Therefore, we can represent a complex prior probability using only a few parameters. Through experiments, we confirmed that our proposed method can acquire the prior probability efficiently and use it to realize highly accurate human detection.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play an important role in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high prior probability is assigned to an area having the same color as past human trajectories. We use a particle filter for representing and updating the prior probability. Therefore, we can represent a complex prior probability using only a few parameters. Through experiments, we confirmed that our proposed method can acquire the prior probability efficiently and use it to realize highly accurate human detection.
• 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: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications（CVA）; 巻 2, p. 145-155, 発行日 2010-11-10
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会