基於改良式HOG之物件追蹤演算法

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魏國鈞(Kuo-Chun Wei) 查詢紙本館藏

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通訊工程學系在職專班

論文名稱

基於改良式HOG之物件追蹤演算法

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摘要(中)

摘要
物件追蹤在近年來是一個相當熱門話題，特別在電腦視頻領域上，以物件追蹤為基礎用在自動監控系統上更是活躍，其中HOG演算法是目前公認最有效的演算法。
實際應用上HOG佔據太多運算，又以Gamma correction最為耗時。本研究本論文設計MHOG(Modified Histogram of Oriented Gradient)，提出用Sobel Edge演算法的概念來延伸來進行取代，修改HOG耗費資源部分，提出一個試用及時系統演算法，稱之為基於改良式HOG之物件追蹤演算法MHOG(Modified Histogram of Oriented Gradient)和HOG相比的優點包括:

1.更能減少運算資源。
2.提升追蹤穩定度
在實驗結果上，展示出原始HOG與本研究所改良之差異性。依序是六個測試影像水鳥、汽車(靜止)、計算機、汽車(移動中)、黑猩猩
?

摘要(英)

Abstract
Object tracking has recently been a widely researched topic, specifically in studies investigating computer-automated surveillance systems in the field of computer vision. Among currently available algorithms , the histogram of oriented gradients (HOG) has been recognized as the most effective design.
However, in practice, applying the HOG requires excessive computational resources, particularly for the gamma correction method, which is the most time-consuming method. Therefore, by expanding the concept of Sobel edge detection, the present study proposed a feasible real-time computing system named the modified histogram of oriented gradients (MHOG) to minimize the resource requirements of the conventional HOG. Comparing the MHOG and HOG revealed that the proposed algorithm has lower computational resource requirements and enhances the stability of tracking.
In this thesis, the differences between the conventional HOG and MHOD are demonstrated in six experimental images, namely images of a water bird, a stationary automobile, a calculator, a moving automobile, and a chimpanzee.

關鍵字(中)

★ 物件追蹤

關鍵字(英)

★ HOG

論文目次

摘要………………………………………………………………………iii
Abstract………………………………………………………………iv
致謝 ……………………………………………………………………v
目錄……………………………………………………………………vi
附圖索引……………………………………………………………………viii
第一章緒論………………………………………………………………1
1.1簡介……………………………………………………………1
1.2研究動機………………………………………………………2
1.3本論文架構……………………………………………………3
第二章過去相關研究…………………………………………3
第三章特徵擷取技術 ………………………………………3
第四章系統架構與改良式HOG演算法………………………3
第五章實驗結果 ……………………………………………3
第六章結論 …………………………………………………3
第二章過去相關研究……………………………………………………4
2.1 物件追蹤……………………………………………………4
2.2 人臉偵測……………………………………………………7

第三章特徵擷取技術………………………………………………………9
3.1 Pure pixel…………………………………………………… 9
3.2 Sobel Edge……………………………………………………11
3.3 Haar-like…………………………………………………… 12
3.4 LBP(Local binary pattern)…………………………………13
3.5 SIFT/SURT ………………………………………………… 15
3.6 HOG(Histogram of Oriented Gradient)…………………… 20
第四章系統架構與改良式HOG演算法…………………………………26
4.1 OpenCV介紹…………………………………………………26
4.2 模擬系統架構 ……………………………………………… 28
4.3 新提出的方法(MHOG)………………………………………30
第五章實驗結果………………………………………………………… 36
5.1 測試影像與實驗參數說明………………………………… 36
5.2 視覺分析…………………………………………………… 36
5.3 量化分析…………………………………………………… 43
第六章結論……………………………………………………………… 45
參考文獻…………………………………………………………… 48

參考文獻

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指導教授

張寶基、范國清

審核日期

2017-1-24

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