關鍵字查詢 | 類別:會議論文 | | 關鍵字:Real-time multiple tracking using a combined technique

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序號 學年期 教師動態
1 93/2 資工系 譚家棟 助理教授 會議論文 發佈 Real-time multiple tracking using a combined technique , [93-2] :Real-time multiple tracking using a combined technique會議論文Real-time multiple tracking using a combined techniqueHsu, Hui-Huang; Shih, Timothy K.; Tang, Chia-Tong; Liao, Yi-Chun淡江大學資訊工程學系Object tracking;Occlusion detection;Object identificationLos Alamitos, California:Institute of Electrical and Electronics Engineers (IEEE)Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, vol.1, pp.111-116IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung UniversityThis paper considers a real-time multi-object tracking algorithm for rigid and non-rigid objects. The major components of the tracking object system are extraction of the background image, adaptation of the background image, and identification of the extracted object. In each component, we improved existing methods without increasing the complexity of computation. The system was tested on our fish tank experiment that solves dynamic occlusions problems.tku_id:
2 93/2 資工系 廖逸群 講師 會議論文 發佈 Real-time multiple tracking using a combined technique , [93-2] :Real-time multiple tracking using a combined technique會議論文Real-time multiple tracking using a combined techniqueHsu, Hui-Huang; Shih, Timothy K.; Tang, Chia-Tong; Liao, Yi-Chun淡江大學資訊工程學系Object tracking;Occlusion detection;Object identificationLos Alamitos, California:Institute of Electrical and Electronics Engineers (IEEE)Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, vol.1, pp.111-116IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung UniversityThis paper considers a real-time multi-object tracking algorithm for rigid and non-rigid objects. The major components of the tracking object system are extraction of the background image, adaptation of the background image, and identification of the extracted object. In each component, we improved existing methods without increasing the complexity of computation. The system was tested on our fish tank experiment that solves dynamic occlusions problems.tku_id:
3 93/2 資工系 施國琛 教授 會議論文 發佈 Real-time multiple tracking using a combined technique , [93-2] :Real-time multiple tracking using a combined technique會議論文Real-time multiple tracking using a combined techniqueHsu, Hui-Huang; Shih, Timothy K.; Tang, Chia-Tong; Liao, Yi-Chun淡江大學資訊工程學系Object tracking;Occlusion detection;Object identificationLos Alamitos, California:Institute of Electrical and Electronics Engineers (IEEE)Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, vol.1, pp.111-116IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung UniversityThis paper considers a real-time multi-object tracking algorithm for rigid and non-rigid objects. The major components of the tracking object system are extraction of the background image, adaptation of the background image, and identification of the extracted object. In each component, we improved existing methods without increasing the complexity of computation. The system was tested on our fish tank experiment that solves dynamic occlusions problems.tku_id:
4 93/2 資訊系 許輝煌 教授 會議論文 發佈 Real-time multiple tracking using a combined technique , [93-2] :Real-time multiple tracking using a combined technique會議論文Real-time multiple tracking using a combined techniqueHsu, Hui-Huang; Shih, Timothy K.; Tang, Chia-Tong; Liao, Yi-Chun淡江大學資訊工程學系Object tracking;Occlusion detection;Object identificationLos Alamitos, California:Institute of Electrical and Electronics Engineers (IEEE)Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, vol.1, pp.111-116IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung UniversityThis paper considers a real-time multi-object tracking algorithm for rigid and non-rigid objects. The major components of the tracking object system are extraction of the background image, adaptation of the background image, and identification of the extracted object. In each component, we improved existing methods without increasing the complexity of computation. The system was tested on our fish tank experiment that solves dynamic occlusions problems.tku_id:
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