整合式的全周監視與辨識系統

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姓名

何世偉(Shih-Wei Ho) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

整合式的全周監視與辨識系統
(Integrated Surrounding Monitor and Recognition System)

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★ 建立在動態載入多重解析度地形模塑的戰術模擬	★ 以多階分割的空間關係做人臉偵測與特徵擷取
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摘要(中)

近年來，防盜的意識逐漸提高，各家廠商也不斷推陳出新，使得監控設備日益普及，在社區、大樓隨處可看見這些設備的身影。目前大樓的監視系統多半是透過多台攝影機安裝在各角落並使用不同角度來達到監控環境的目的，如此一來，不僅警衛要隨時注意每一個畫面的影像，且沒有統一空間的觀念，管理起來較不方便。
相較於一般視角的相機，魚眼相機的視角可高達 180 o，能拍攝到更寬廣的範圍；因此在相同的監控環境中，若使用魚眼相機可使設備數量大幅減少，進而降低系統建構及管理的成本。本研究使用魚眼相機作為主要的監控設備，提出整合式的全周監視與辨識系統；整個系統包含兩大部份：一是全周監視用於監視建築周遭的狀況，二是線上偵測前景物並分辨入侵者，若有異狀則透過物件辨識確認為入侵者後發出警示。
在全周監視部份，我們在建築的周圍各架設一台魚眼相機向下 25 o 拍攝，並在離線程序中利用所拍攝的多張不同入侵者位置，根據這些特徵點在影像上的座標求得地圖座標與影像之間的相對關係，再透過座標換算矩陣將物體的位置標示在地圖影像上，產生即時的全周監視影像。
為了避免不必要的警示，例如：車輛、貓狗經過…等，因此在本論文中加入了物件辨識系統，當偵測到前景物時，會擷取前景物的特徵做物件辨識確認為入侵者後，才會在畫面上顯示警示訊息通知使用者，藉此減少不必要的警示與人力資源的浪費。
本研究的系統使用兩部魚眼相機，並使用多段具有不同天候情況影片做實驗；在偵測方面，有 583 個樣本，平均有 96.5 % 的偵測率；在辨識方面，共有 681 個入侵者樣本，辨識正確率可達 93.8 %，誤判率為 2.53 %。錯誤辨識發生的原因是雨天的地面積水反射人影或前景物移動速度過快所造成的。

摘要(英)

In recent years, as the security awareness gradually increase and various manufacturers have been introducing new products, the monitoring devices become more prevalent. No matter where you are, you can see these devices, such as communities and buildings. Currently, most of the monitoring system of the building installation through multiple cameras at each corner and use different angles to achieve the purpose of monitoring the environment, as a result, not only to pay attention to guard image per screen at any time, and there is no concept of a unified space, and the management less convenient.
The view angle of a fisheye camera is 180 degree, so it can cover a wider field of view than a normal camera. Thus, in the same surveillance environment, only a few fisheye cameras can replace many traditional cameras to survey the events; such that the cost of system construction and management are then reduced. We use the fisheye camera as our main monitoring device, and propose integrated surrounding monitor and recognition system. The proposed system is composed of two major modules: surrounding monitor and online recognition system.
In the surrounding monitor module, we mount the cameras around the building and tilt 25 degrees. According to the relationship between the image plane and the surrounding map, we can solve the homography matrix and point the intruder on the surrounding map.
In recognition system, when a foreground object is detected, we extract the foreground object’s feature to recognize whether it’s intruder. If it were an intruder, the system will show alarm message on the screen to notice the user. Through the recognition system we can reduce most of unnecessary human resources.
We conducted experiments with the proposed system on several videos. The experiments results show that the average detection rate is 96.5 percent with 583 samples, the recognition rate can up to 93.8 percent with 681 samples and the average false positive rate is 2.53 percent.

關鍵字(中)

★ 物件偵測
★ 物件辨識
★ 全周監視

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iv
圖目錄 vii
表目錄 x
第一章緒論 1
1.1 研究動機 1
1.2 系統概述 3
1.3 論文架構 4
第二章相關研究 6
2.1 前景物偵測 6
2.2 特徵擷取 7
2.2.1 Haar-like feature 7
2.2.2 HOG feature 8
2.3 物件辨識 10
2.3.1 AdaBoost 簡介 10
2.3.2 樣板比對 12
2.4 多相機追蹤技術 15
第三章前景物偵測 19
3.1 codebook 背景模型 19
3.1.1 codebook背景模型的介紹 19
3.1.2 色彩與亮度的計算 20
3.1.3 codebook 背景模型的初始化與建立 22
3.1.4 前景偵測 24
3.2 ViBe背景模型 24
3.2.1 ViBe背景模型的介紹 25
3.2.2 ViBe 模型的初始化 26
3.2.3 ViBe 模型的更新方式 26
3.3 多重群聚區塊中的重疊區塊處理 27
3.4 各種背景模型之比較 29
第四章物件辨識 31
4.1 HOG 特徵 31
4.2 SVM 分類 34
第五章全周地圖座標換算 37
5.1 座標換算矩陣 37
5.2 相機內外部參數求解座標換算矩陣 38
5.3 特徵點對應求解座標換算矩陣 39
第六章實驗 41
6.1 實驗環境 41
6.2 物件偵測實驗結果分析 43
6.3 物件辨識實驗結果分析 47
6.4 全周地圖影像 53
第七章結論與未來展望 56
參考文獻 57

參考文獻

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指導教授

曾定章(Din-Chang Tseng)

審核日期

2016-8-8

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