🏛️國立陽明交通大學機構典藏

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Computer-assisted yoga training system
(2018-09-01)
Chen, Hua-Tsung
;
He, Yu-Zhen
;
Hsu, Chun-Chieh
;
資訊工程學系
;
Department of Computer Science
Self-training is essential in sports exercise. However, without the instruction of a coach, a practitioner may progress to a limited extent. Improper postures may even cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a yoga self-training system, which aims at instructing the practitioner to perform yoga poses correctly, assisting in rectifying poor postures, and preventing injury. Integrating computer vision techniques, the proposed system analyzes the practitioner's posture from both front and side views by extracting the body contour, skeleton, dominant axes, and feature points. Then, based on the domain knowledge of yoga training, visualized instructions for posture rectification are presented so that the practitioner can easily understand how to adjust his/her posture. Experiments on twelve yoga poses performed by different practitioners validate the feasibility of the proposed system in yoga training.
2D Histogram-based player localization in broadcast volleyball videos
(2016-06)
Hsu, Chun-Chieh
;
Chen, Hua-Tsung
;
Chou, Chien-Li
;
Lee, Suh-Yin
;
資訊工程學系
Player location is one of the most informative cues for obtaining tactics arrangement and collecting descriptive game statistics. However, state-of-the-art supervised learning-based methods for player localization require a large amount of labeled training data. Hence, the development of automatic systems for player localization becomes indispensable. Volleyball games reach a huge audience base and contain a variety of tactical strategies, necessitating the implementation of systems for inferring tactics and analyzing formations automatically. Therefore, a novel 2D histogram-based player localization method capable of locating players with occlusions is developed and presented in this paper. The proposed system is able to automatically detect the court lines for camera calibration, extract players by calculating both x and y histograms of extracted player masks, and visualize the team formations on real-world court model. The experiments on broadcast volleyball videos demonstrate efficient and effective results against a traditional object segmentation method (connected component analysis) and a supervised learning approach utilizing histogram of oriented gradient features.
Incorporating frequent pattern analysis into multimodal HMM event classification for baseball videos
(2016-05)
Chen, Hsuan-Sheng
;
Tsai, Wen-Jiin
;
資訊工程學系
;
Department of Computer Science
Data mining and frequent pattern analysis have recently become a popular way of discovering new knowledge from a data set. However, it is rarely applied to video semantic analysis. Therefore, this paper introduces two methods: frequent-pattern trained HMM and frequent-pattern tailored HMM to incorporate frequent pattern analysis into multimodal HMM event classification for baseball videos. Besides, different symbol coding methods including temporal sequence coding and co-occurrence symbol coding for multimodal HMM classification are compared. The results of our experiments on baseball video event classification demonstrate that integration of frequent pattern analysis could help to improve event classification performances.
多磁碟架構上多媒體資料存取之研究
(1996)
李素瑛
;
LEE SUH-YIN
;
國立交通大學資訊工程學系
網路多媒體系統輔助廢棄物環境資訊檢索及查詢(I)
(1997)
高正忠
;
Jen-Jung Kao
;
交通大學
多媒體系統晶片設計技術之研究---子計畫I：用於多媒體系統晶片之模組設計與功能協調(I)
(1999)
任建葳
;
交通大學電子工程研究所

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🏛️國立陽明交通大學機構典藏

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