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博碩士論文 etd-0307116-213744 詳細資訊
Title page for etd-0307116-213744
論文名稱
Title
以影像處理為基礎之公車自動乘客計數系統設計
Automatic Passenger Counting System Design for Bus Using Image Processing Techniques
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
86
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-07-26
繳交日期
Date of Submission
2016-09-06
關鍵字
Keywords
背景相減、人數計數、K-means分群、倒傳遞類神經網路、物件追蹤、行人偵測
back propagation neural network, people detection, people counting, K-means cluster, background subtraction, object tracking
統計
Statistics
本論文已被瀏覽 5660 次,被下載 28
The thesis/dissertation has been browsed 5660 times, has been downloaded 28 times.
中文摘要
本論文設計一套適應性乘客計數系統應用於公車場景,並整合全球定位系統(GPS)之座標資訊與標準時間,得以建立出在不同時段公車於各站牌上下車的乘客數,藉此推算出車內人數與空位數量提供業者相關的乘客數資訊,期望可以規劃出適當的車輛班次及派遣。

人數計數是很多影像監視系統中重要的應用之一,在本論文中,我們提出藉由背景相減法以及物件追蹤的方式來達到自動化乘客計數。本論文以背景相減法擷取出移動前景影像,此時的前景影像還含有許多雜訊,且移動物件有破碎不連續的情形,因此利用影像形態學修補物件,以連通物件標記法去除面積較小之物件達到消除雜訊的目的,即可得到有效的前景影像。藉由背景相減法取出移動物件,使得前景影像能以連通成分的形式表示。接著,行人偵測演算法通過倒傳遞類神經網路所建立之行人模型,判斷移動物件是否為人。最後以K-means分群演算法區分出個別行人的位置,而每一個連通物件的質心則是用來做 跟蹤的使用以達到正確的計數。
Abstract
This paper to design an adaptive passenger counting system is applied to the public transportation vehicle: bus. The system combines Global Positioning System (GPS) and standard time to establish number of passengers for each bus stop in different period time and calculates the number of the passengers and unoccupied seats to provide Bus service company business-related information.

People counting is one of many important applications of video surveillance systems. In this paper, we propose an automatic people counting system by applying background subtraction and object tracking on video camera output. The foreground is extracted by background subtraction. Morphological operations are used to repair damage to the image and connected-component labeling method is used to eliminate of noise. Background subtraction identifies moving objects in the form of connected components. The people detection algorithm attempts to determine whether moving objects correspond to the human model that established by back propagation neural network. K-means clustering is used to enable the segmentation of single persons from connected-component foreground. Each centroid of foreground objects is used to track moving objects for counting.
目次 Table of Contents
論文審定書 i
誌 謝 ii
中文摘要 iii
Abstract iv
目 錄 v
圖 次 vii
表 次 x
第一章 緒論 1
1-1 前言 1
1-2 研究動機 1
1-3 文獻回顧 3
1-4 論文貢獻 8
1-5 章節介紹 8
第二章 系統概述 9
2-1 系統架構簡介 9
2-2 系統平台 11
2-2-1硬體設備與作業系統 11
2-2-2軟體開發套件 14
第三章 系統設計與實現 17
3-1 移動物件萃取 17
3-1-1 背景相減 17
3-2 影像強化 24
3-2-1 形態學 25
3-3 行人模型 31
3-3-1 影像特徵擷取 33
3-3-2 去除非人特徵 34
3-3-3 倒傳遞類神經網路[31] 35
3-3-4 K-means分群[34] 38
3-4 乘客追蹤與計數 39
3-4-1 乘客追蹤 39
3-4-2 乘客計數 41
3-5 車門狀態偵測 42
3-5-1 邊緣偵測[34] 42
3-5-2 模板比對[21] 43
第四章 實驗結果 44
4-1 實驗場景介紹 44
4-1-1 實驗一 46
4-1-2 實驗二 48
4-2 影像前景擷取 50
4-3 行人模型建立 52
4-4 車門偵測結果 55
4-5 乘客計數結果 59
4-5-1 實驗一 60
4-5-2 實驗二 62
4-6 實驗結果與比較 66
4-7 人機介面設計及其應用 68
第五章 結論與未來展望 70
5-1 結論 70
5-2 未來展望 71
參考文獻 72
參考文獻 References
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