臺灣博碩士論文加值系統

隨著數位資訊化的演進，社會對監控器的需求量也與日俱增。監視器拍攝的影像傳回監控主機端，在藉由人力監督的方式來達到其監控的目的。隨著監控攝影機數量的增加，單一人力監控數十個監控畫面的方式仍然顯得吃重、且容易因視覺疲勞遺漏重要資訊，為了達成此目的，我們讓攝影機擁有前處理的能力，在智慧監控系統中，如何在多個物件交錯時達到追蹤的效果一直是個挑戰，本論文提出一個智慧型監控系統架構設計，主要由四種影像處理模組組成，包含Foreground Detection、Sliced Connected component Labeling、Object Grouping及Object Tracking，將上述四種演算法以硬體架構實現，我們以TSMC 90nm的製程去實現我們的設計，我們的設計操作在4MHz在不包含memory的情況下約為18.71K的gate count，消耗功率約為11.4037mW，memory使用量為92.288Kbytes。只需利用物件的中心點以及邊框就能夠追蹤物件，並且解決交錯時發生的問題。

The digital surveillance system becomes more and more popular in recent years. It attempts to raise amount of high resolution cameras, consequently those systems stupendously increase the computational load on central server. As in the intelligent object recognition processing flow, the technique on segmentation and tracking multiple targets, such as tracking group of people through occlusion is still challenging. In this paper, we present an architecture design for intelligent surveillance system. Mainly made up of four image processing module composed, contains foreground detection, sliced connected component labeling, object grouping and object tracking. We have a complete system-level solution on algorithm and VLSI implementation. This design is using TSMC 90 nm library with 4 MHz operation frequency. Without calculating memory of gate count about 18.71K. Power consumption about 11.4037mW and memory usage is 92.288Kbytes. Simply use the center and boundary box of the object will be able to track objects, and solve the problem occurs when occlusion.

摘要 I
ABSTRACT II
TABLE OF CONTENTS III
LIST OF FIGURES V
LIST OF TABLES VII
CHAPTER 1 Introduction 1
1.1 MOTIVATION 1
1.2 OBJECT TRACKING OVERVIEW 3
1.3 THESIS ORGANIZATION 5
CHAPTER 2 Overall of the Proposed Architecture Design for Intelligent Surveillance System 6
2.1 FOREGROUND DETECTION 8
2.1.1. Algorithm of Background Subtraction 8
2.1.2. Architecture of Background Subtraction 9
2.2 SLICED CONNECTED COMPONENT LABELING 10
2.2.1. Algorithm of Object Labeling 10
2.2.2. Architecture of Sliced Connected Component Labeling 16
2.3 OBJECT GROUPING 20
2.3.1. Algorithm of Object Grouping 20
2.3.2. Architecture of Object Grouping 23
2.4 OBJECT TRACKING 24
2.4.1. Algorithm of Object Tracking 24
2.4.2. Architecture of Object Tracking 30
2.5 APPLICATION 32
2.5.1. Vision Based Indoor Positioning 32
CHAPTER 3 Experimental Results 35
3.1 ANALYZE THE WHOLE SYSTEM 35
3.2 RESULT OF OBJECT TRACKING 36
3.3 CHIP SPECIFICATION 38
3.4 RESULT OF INDOOR POSITIONING 40
CHAPTER 4 Conclusion 45
REFERENCES 47

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