Review on Kernel based Target Tracking for Autonomous Driving

• Item type: Journal(1)
• 公開日: 2016-01-15
• タイトル: Review on Kernel based Target Tracking for Autonomous Driving
• タイトル / 言語: en
• タイトル: Review on Kernel based Target Tracking for Autonomous Driving
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：スマートコミュニティ実現のための高度交通システムとモバイル通信] visual tracking, kernel-based tracking, kernel-based learning
• 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: Tongji University, School of Electronics and Information
• 著者所属: Tongji University, School of Electronics and Information
• 著者所属: Tongji University, School of Electronics and Information
• 著者所属: Kumamoto University, Department of Computer Science
• 著者所属(英): en
• 著者所属(英): Tongji University, School of Electronics and Information
• 著者所属(英): en
• 著者所属(英): Tongji University, School of Electronics and Information
• 著者所属(英): en
• 著者所属(英): Tongji University, School of Electronics and Information
• 著者所属(英): en
• 著者所属(英): Kumamoto University, Department of Computer Science
• 著者名: Yanming, Wang; Jiguang, Yue; Yanchao, Dong; Zhencheng, Hu
• 著者名(英): Yanming, Wang; Jiguang, Yue; Yanchao, Dong; Zhencheng, Hu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Significant progress has been made in the field of autonomous driving during the past decades. However, fully autonomous driving in urban traffic is still extremely difficult in the near future. Visual tracking of vehicles or pedestrians is an essential part of autonomous driving. Among these tracking methods, kernel-based object tracking is an effective means of tracking in video sequences. This paper reviews the kernel theory adopted in target tracking of autonomous driving and makes a qualitative and quantitative comparison among several well-known kernel based methods. The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target tracking in the practical application environments of autonomous driving. This paper reports on the result of evaluating the performances of five algorithms by using seven video sequences.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.49; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Significant progress has been made in the field of autonomous driving during the past decades. However, fully autonomous driving in urban traffic is still extremely difficult in the near future. Visual tracking of vehicles or pedestrians is an essential part of autonomous driving. Among these tracking methods, kernel-based object tracking is an effective means of tracking in video sequences. This paper reviews the kernel theory adopted in target tracking of autonomous driving and makes a qualitative and quantitative comparison among several well-known kernel based methods. The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target tracking in the practical application environments of autonomous driving. This paper reports on the result of evaluating the performances of five algorithms by using seven video sequences.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.49; ------------------------------
• 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 57, 号 1, 発行日 2016-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764