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系統識別號 U0026-0812200914310334
論文名稱(中文) 高效率多方向輪椅偵測系統
論文名稱(英文) An Efficient Multi-Directional Wheelchair Detection System
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 96
學期 2
出版年 97
研究生(中文) 曾聖傑
研究生(英文) Sheng-Chieh Tseng
學號 q3695426
學位類別 碩士
語文別 英文
論文頁數 42頁
口試委員 口試委員-黃春融
口試委員-張傳育
指導教授-詹寶珠
口試委員-張建禕
口試委員-孫明廷
中文關鍵字 多方向  輪椅偵測 
英文關鍵字 wheelchair detection  boosted cascade  decision tree 
學科別分類
中文摘要 這篇研究的目的是想要提供一個高效率多方向輪椅偵測系統。我們使用二種不同特性的特徵來描述輪椅。為了處理輪椅在各種方向視覺上的變化並增進偵測的效能,我們建立了一個基於多方向串接促進器及決策樹的架構的多方向輪以偵測系統。利用這個系統可以很快的去除非輪椅的區域同時偵測輪椅。並能建立一個追蹤系統,去輪椅使用者的照護輪椅使用者的行動。在我們的實驗可以達到百分之九十以上的準確率。
英文摘要 The purpose of this paper is to provide a fast wheelchair detection system. Two features with different-properties are used to represent wheelchairs. To overcome the variable appearances of wheelchairs due to viewing directions and perform a fast detection, a multi-view wheelchair detection system is proposed based on the concepts of the cascade boosting and the decision tree. This system can quickly remove obvious non-wheelchair regions and obtain the moving direction of the detected wheelchair at the same time. When a wheelchair is detected, a tracking procedure is performed to monitor the wheelchair user. Through our experiments, we find that the wheelchair detection rate can achieve over 90 %.
論文目次 Chapter 1 Introduction ........................................1
Chapter 2 Related Works .......................................4
Chapter 3 Multi-Directional Wheelchair Detection Framework ....7
3.1 Decision tree structure ...................................7
3.2 Boosted classifier as Tree node...........................10
3.2.1 Boosted cascade classifier..............................11
3.2.2 Feature pool creation...................................15
3.3 Training implementation...................................19
3.3.1 Feature creation........................................20
3.3.2 Training data of cascade................................21
3.3.3 Train boosted cascade classifiers ......................21
3.4 Detection process ........................................23
3.5 Multi-directional tracking ...............................24
Chapter 4 Experimental Results................................29
4.1 The results of different features with each direction.....29
4.2 The result of multi-directional detection.................35
4.3 The result of multi-directional tracking .................37
Chapter 5 Conclusion..........................................38
References....................................................39
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