臺灣博碩士論文加值系統

在特殊需求之出入口及場所取得進出人員之人流數據乃具非常高的參考價值。然而傳統使用人工計數方式除了需高人力成本外，且較無法工作於需要長期做計數工作之門口；而機械式旋轉轉軸之計數裝置在裝設上不易，使用上也極為不便，僅能由單人通過計數裝置；利用紅外線或雷射等電子裝置雖然在裝設上比機械式裝置來的簡便許多，但也只能做單人的計數工作，仍然不能使用於高人流之情況。而採用攝影機利用電腦視覺等視訊處理技術，其所需裝置不僅成本低廉，安裝容易外，更可獲得較大範圍之資訊來做計數偵測，以達到高人流應用之需求，並可配合數位監錄系統等研究，為目前智慧型監控系統技術之一。
本論文利用視訊處理方式，提出一具有雙向計數功能之計數系統，並可應用於高人流之進出口，以符合實際應用上之需求。首先，我們利用架設於門口正上方且鏡頭垂直下照攝之攝影機，攝取人流進出之情形；其後利用移動物切割演算法，擷取監視區域內之人身圖形，並依面積估測法則做人身圖形之面積判斷，我們即可得到一初始之人流計數結果。我們並依據其顏色做HSI直方圖統計，將其量化後可獲得一組色彩向量(color-vector)以用來追蹤人流之移動方向，並可以修正初期之人流計數結果。此外，因人流之觸碰而造成人身影像間之嚙合(occlusion)現象，在本系統中也能夠加以克服。
最後，我們將實驗結果與人工計數之結果做比較，本系統在正常之情況下，其精確度可以達到95%左右，並可符合即時(Real-time)之需求。

The statistics of pedestrian movement which to be obtained from particular entrances and occasions are highly consultable. However, the manual counting method takes highly manpower costs. The rotating axles are not easy to be assembled and only one person can pass it at a time. By using electronic mechanisms, such as infrared/ultrared rays; the problem regarding number of persons still remaining unsolved. Due to the above reasons, we propose a novel automatic people-counter approach based on video processing technique. The cost of this mechanism is more inexpensive and easy to be assembled. Furthermore, the problem regarding high density of people can be solved by using this mechanism.
This thesis proposed a bi-directional people counting method which can be applied to situation such as high people-flow and can be utilized in real-time applications. First of all, the passing people are roughly counted with the area of people projected on an image captured by a zenithal video camera. The moving direction of the pedestrian can be recognized by tracking pattern of each person by analyzing its HSI histogram. Besides, the occlusion problem of both people touching together and merge/split phenomenon can be overcome in this system.
Finally, the experimental result shows that the accuracy is about 95%, and this method can meet the requirement of Real-time.

摘 要 i
英語摘要 ii
誌 謝 iii
目 錄 iv
圖 目 錄 vi
第一章 緒 論 1
1.1 研究背景 1
1.2 相關研究 2
1.2.1 移動物偵測 2
1.2.2 人體追蹤 4
1.2.3 人流計數器 6
1.3 系統架設與流程 8
1.4 論文架構 11
第二章 移動目標偵測 12
2.1 適應性背景相減 12
2.1.1 初始化背景模型 12
2.1.2 前景區域偵測 14
2.1.3 背景模型更新 15
2.2 陰影區域移除 16
2.3 後處理演算法 21
2.3.1 形態運算 22
2.3.2 連通單元標記 25
2.4 多物件切割 27
第三章 人數估測與特徵表示 29
3.1 人數估測 29
3.2 特徵表示 31
3.2.1 HSI色彩模型 32
3.2.2色彩向量 33
第四章　移動物追蹤 39
4.1　追蹤處理 40
4.2　Merge-Split偵測與處理 41
第五章 實驗結果 44
5.1 單人計數 46
5.2 雙人計數 47
5.3 多人雙向計數 48
第六章 結論與未來方向 49
參考文獻 50
相關著作 53

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