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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/35240

    Title: 運動影片中人物動作的自動偵測與分析 : 以立定跳遠為例
    Other Titles: Automatic detection and analysis of human motion in sports video : a case study in standing long jump
    Authors: 謝勝文;Hsieh, Sheng-wen
    Contributors: 淡江大學資訊工程學系碩士班
    許輝煌;Hsu, Hui-huang
    Keywords: 動作分析;人的偵測與追蹤;姿勢預估;基因演算法;Motion analysis;human detection and tracking;shadow removal;pose estimation;genetic algorithms
    Date: 2006
    Issue Date: 2010-01-11 06:15:03 (UTC+8)
    Abstract: 關於各種的運動分析的研究越來越多,如分析網球、籃球、高爾夫球等,都有人在研究分析資料以做為訓練之用,而我們這篇論文所要做的是分析小朋友跳遠的影片,希望可以藉由電腦的幫助,將分析影片的難度降低,更進而讓系統可以提供受測者意見,使之可以了解自己動作的問題而不需要專業的老師在側協助。
    Analyses and researches toward various sport activities are broadly put in use, and these sports involve tennis, basketball, and golf, whose data are collected and analyzed as the training data. In this thesis, we examine the video of children doing long jumps, aiming to reduce the difficulty in analyzing video sequences of children who are doing long jumps with the assistance of the computer. Furthermore, the developed system can also give
    comments on the testers about the problems of their movements and gestures without a professional instructor.
    The proposed method consists of two parts. The first one is to retrieve human objects from the video and it can be divided into five steps -background restoration, foreground objects retrieval, noise reduction, breakage repainting, and shadow removal.
    The second part is to do stick model prediction, which utilizes the characteristics of survival of the fittest in Genetic Algorithm (GA). As a result, we can obtain a stick model that is fittest to the silhouette after several generations of evolutions.
    While the predicted a stick model correspond to the silhouette of testers in the video, a succession of action decomposition is then taken place to analyze the correctness of the movements. Also, a scoring function can be integrated into the system so that the testers can receive feedbacks from the system. They can understand which parts of movements should be improved and the system makes it possible that testers can do self practice even without a professional physical education instructor.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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