關鍵字查詢 | 類別:會議論文 | | 關鍵字:Moving Object Detection and Tracking

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序號 學年期 教師動態
1 97/1 資工系 王駿瑋 助理教授 會議論文 發佈 Moving Object Detection and Tracking , [97-1] :Moving Object Detection and Tracking會議論文Moving Object Detection and TrackingLin, Hwei-Jen; Liang, Feng-Ming; Wang, Chun-Wei; Yang, Fu-Wen淡江大學資訊工程學系Detection;Tracking;Gaussian mixture model (GMM);Particle filter (PF);Sequential K means algorithm;Expectation maximization (EM)臺北縣淡水鎮 : 淡江大學第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.809-813淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通科技管理學系; 中華民國人工智慧學會For object detection and tracking, we use amodified version of Gaussian Mixture Models(GMMs) to construct background, which is thensubtracted from the image to obtain the foregroundwhere the moving objects locate. We then performsome operations, including shadow removal, edgedetection, and connected component analysis, tolocalize each moving object in the foreground. As soon as an object is detected it is then trackedin the following frames by the use of Particle Filters(PF). PF is effective but the dimension of its statespace is high so as the tracked objects tend t
2 97/1 資工系 楊富文 助理教授 會議論文 發佈 Moving Object Detection and Tracking , [97-1] :Moving Object Detection and Tracking會議論文Moving Object Detection and TrackingLin, Hwei-Jen; Liang, Feng-Ming; Wang, Chun-Wei; Yang, Fu-Wen淡江大學資訊工程學系Detection;Tracking;Gaussian mixture model (GMM);Particle filter (PF);Sequential K means algorithm;Expectation maximization (EM)臺北縣淡水鎮 : 淡江大學第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.809-813淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通科技管理學系; 中華民國人工智慧學會For object detection and tracking, we use amodified version of Gaussian Mixture Models(GMMs) to construct background, which is thensubtracted from the image to obtain the foregroundwhere the moving objects locate. We then performsome operations, including shadow removal, edgedetection, and connected component analysis, tolocalize each moving object in the foreground. As soon as an object is detected it is then trackedin the following frames by the use of Particle Filters(PF). PF is effective but the dimension of its statespace is high so as the tracked objects tend t
3 97/1 資工系 林慧珍 教授 會議論文 發佈 Moving Object Detection and Tracking , [97-1] :Moving Object Detection and Tracking會議論文Moving Object Detection and TrackingLin, Hwei-Jen; Liang, Feng-Ming; Wang, Chun-Wei; Yang, Fu-Wen淡江大學資訊工程學系Detection;Tracking;Gaussian mixture model (GMM);Particle filter (PF);Sequential K means algorithm;Expectation maximization (EM)臺北縣淡水鎮 : 淡江大學第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.809-813淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通科技管理學系; 中華民國人工智慧學會For object detection and tracking, we use amodified version of Gaussian Mixture Models(GMMs) to construct background, which is thensubtracted from the image to obtain the foregroundwhere the moving objects locate. We then performsome operations, including shadow removal, edgedetection, and connected component analysis, tolocalize each moving object in the foreground. As soon as an object is detected it is then trackedin the following frames by the use of Particle Filters(PF). PF is effective but the dimension of its statespace is high so as the tracked objects tend t
4 98/1 電機系 江正雄 教授 會議論文 發佈 Low resolution method using adaptive LMS scheme for moving objects detection and tracking , [98-1] :Low resolution method using adaptive LMS scheme for moving objects detection and tracking會議論文Low resolution method using adaptive LMS scheme for moving objects detection and trackingHsia, Chih-Hsien; Yeh, Yi-Ping; Wu, Tsung-Cheng; Chiang, Jen-Shiun; Liou, Yun-Jung淡江大學電機工程學系New York: Institute of Electrical and Electronics Engineers (IEEE)Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on, pp.1-4This paper presents a new model for adaptive filter with the least-mean-square (LMS) scheme to train the mask operation on low resolution images. The adaptive filter theory with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving object detection and tracking. However, the successful moving objects detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. Many approaches have been developed in constrained environments to detect and track moving objects. On the o
5 97/1 電機系 江正雄 教授 會議論文 發佈 Moving Object Tracking Using Symmetric Mask-Based Scheme , [97-1] :Moving Object Tracking Using Symmetric Mask-Based Scheme會議論文Moving Object Tracking Using Symmetric Mask-Based SchemeHsia, Chih-Hsien; Huang, Ding-Wei; Chiang, Jen-Shiun; Wu, Zong-Jheng淡江大學電機工程學系Lifting-based Discrete Wavelet Transform (LDWT);symmetric mask-based discrete wavelet transform (SMDWT);symmetric mask-based scheme (SMBS)New York: Institute of Electrical and Electronics Engineers (IEEE)Information Assurance and Security, 2009. IAS '09. Fifth International Conference on, pp.173-176This work presents a new approach, symmetric mask-based scheme (SMBS), for moving object detection and tracking based on the symmetric mask-based discrete wavelet transform (SMDWT). This work presents a fast algorithm, called 2D SMDWT, to improve the critical issue of the 2D lifting-based Discrete Wavelet Transform (LDWT), and then obtains the benefit of low latency, reduced complexity, and low transpose memory for object detection. The successful moving object detection in a real surrounding environm
6 97/1 電機系 夏至賢 助理教授 會議論文 發佈 Moving Object Tracking Using Symmetric Mask-Based Scheme , [97-1] :Moving Object Tracking Using Symmetric Mask-Based Scheme會議論文Moving Object Tracking Using Symmetric Mask-Based SchemeHsia, Chih-Hsien; Huang, Ding-Wei; Chiang, Jen-Shiun; Wu, Zong-Jheng淡江大學電機工程學系Lifting-based Discrete Wavelet Transform (LDWT);symmetric mask-based discrete wavelet transform (SMDWT);symmetric mask-based scheme (SMBS)New York: Institute of Electrical and Electronics Engineers (IEEE)Information Assurance and Security, 2009. IAS '09. Fifth International Conference on, pp.173-176This work presents a new approach, symmetric mask-based scheme (SMBS), for moving object detection and tracking based on the symmetric mask-based discrete wavelet transform (SMDWT). This work presents a fast algorithm, called 2D SMDWT, to improve the critical issue of the 2D lifting-based Discrete Wavelet Transform (LDWT), and then obtains the benefit of low latency, reduced complexity, and low transpose memory for object detection. The successful moving object detection in a real surrounding environm
7 98/1 電機系 夏至賢 助理教授 會議論文 發佈 Low resolution method using adaptive LMS scheme for moving objects detection and tracking , [98-1] :Low resolution method using adaptive LMS scheme for moving objects detection and tracking會議論文Low resolution method using adaptive LMS scheme for moving objects detection and trackingHsia, Chih-Hsien; Yeh, Yi-Ping; Wu, Tsung-Cheng; Chiang, Jen-Shiun; Liou, Yun-Jung淡江大學電機工程學系New York: Institute of Electrical and Electronics Engineers (IEEE)Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on, pp.1-4This paper presents a new model for adaptive filter with the least-mean-square (LMS) scheme to train the mask operation on low resolution images. The adaptive filter theory with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving object detection and tracking. However, the successful moving objects detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. Many approaches have been developed in constrained environments to detect and track moving objects. On the o
8 97/1 資工系 林慧珍 教授 會議論文 發佈 Moving Object Detection and Tracking , [97-1] :Moving Object Detection and Tracking會議論文Moving Object Detection and TrackingLin, Hwei-jen; Liang, Feng-ming; Wang, Chun-wei; Yang, Fu-wen淡江大學資訊工程學系Proceedings of the 13th Conference on Artificial Intelligence and Applications (TAAI2008)淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通訊科技管理學系; 中華民國人工智慧學會tku_id: 000086204;Submitted by 曉芬 游 (139570@mail.tku.edu.tw) on 2011-10-05T14:22:41Z No. of bitstreams: 0;Made available in DSpace on 2011-10-05T14:22:41Z (GMT). No. of bitstreams: 0;20121019-補正完成by Xiaoen國內蘭陽校園20081121~20081122YTWNThe 13th Conference on Artificial Intelligence and Applications (第十三屆人工智慧與應用研討會, TAAI2008)I-Lan, Taiwan<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/59867</url></record></links>
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