利用平均移動法及特徵人形模組之行人偵測與追蹤

Pedestrian Detection And Tracking Using Mean Shift Algorithm And A Human Eigen Shape Model

馬飛和(Freida Consuelo Palma)

指導教授 ： 賴尚宏

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2007年)

摘要

我們利用廣為人用的PCA統計模型建構出的特徵人形模組，結合平均移動追蹤法發展出一套新的追蹤人形的方法。一般的平均移動追蹤注重在藉由利用矩形形狀的追蹤。我們的新方法注重在利用更精確的人形預測去貫穿整部影片地追蹤人形。這整篇論文著重在找出人形的位置，辨別前景物體是否為人形，用改進過的平均移動法來追蹤辨認出的人形物體。最後是改進過的平均移動追蹤法中的人形主成分係數的更新方法，用以當人形形狀改變時更新它。

關鍵字

平均移動 ； 特徵人形模組

並列摘要

A new approach to human tracking was developed through the use of a popular statistical model, known as Principal Component Analysis, for constructing the human eigen-shape model in conjunction with the mean shift tracking algorithm. The regular mean shift tracker focuses on tracking through the use of the rectangle shape. The new approach focuses on using a more precise estimation of a human shape to track the human throughout a video sequence. The entire paper focuses on the localization of human; identification of the human; tracking the identified human by the modified mean shift algorithm. Last but not least, is the updating of the human principal component coefficients in the modified mean shift tracker, so as to update the human shape as it changes.

並列關鍵字

mean shift ； eigen-shape model

參考文獻

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