本論文將提供一個具有低成本且有效的二維離散小波轉換應用於智慧型視訊監控系統。其目的是將此技術應用於視訊壓縮以及電腦視覺中，利用離散小波轉換的特色發展出低轉置記憶體的超大型積體電路架構以及快速物件偵測演算法，以達到智慧型視訊監控系統的規格，並且兼具有簡單性、即時性、以及安全性的功能。在本論文中，我們將研究提升式離散小波轉換所產生出的缺點進而改良其演算法，再應用於智慧型視訊監控系統與視訊資料壓縮技術中並且達到即時處理。
    小波轉換已經在影像壓縮與處理中日漸重要，它允許在空間與頻率同時分析以及可調性視訊處理。基於離散小波轉換的特性，在本文中提出兩個新的方法：首先，我們提出新的二維雙模組提升式離散小波轉換硬體架構；在一般的二維提升式離散小波轉換中會有大量使用其轉置記憶體的缺點，其提出交錯讀取掃瞄演算法，以支援高解析度視訊以及減低轉置記憶體的成本，達到謹2N與4N(5/3或9/7模式)的使用量；另外，本方法以平行以及管線架構來增加運算時間，使其適用於Motion-JPEG2000。第二，我們提出對稱式遮罩法並利用於智慧型視訊監控系統；此方法改善二維提升式離散小波轉換的問題，以達到低延遲、低複雜度、以及低轉置記憶體；它可以達到低複雜度離散小波轉換運算以提高準確度與即時性的移動物件偵測系統，在測試的16個場景中，平均物件偵測的準確率與處理速度各別為89.59%以及每秒可執行47.1張影格數，並且應用於連續偵測之室內、外、以及日、夜間場景。
    在上述的兩種方法中均是以提升式離散小波轉換的改良為基礎。在實驗結果中得知，我們所提出的方法可以提供低轉置記憶體以及即時處理之效果應用於高解析度智慧型視訊監控系統。

This thesis attempts to develop a low cost, practical application of a two-dimensional (2-D) discrete wavelet transform (DWT) to an intelligent video surveillance system. The goal is to combine the video compression and computer vision processing used in a wavelet-based system and to adopt DWT to develop a low transpose memory VLSI architecture and a fast object detection algorithm that can meet the specifications of intelligent video surveillance and can also process information easily, in real-time, and with safe functioning. This thesis investigates the fundamental concept behind the wavelet transform and provides an overview of some improved algorithms for lifting-based DWT (LDWT). The video surveillance system is able to detect moving object events, and video compression information is captured by surveillance cameras in real-time.
  Wavelet transformation has become increasingly important in image compression and processing since wavelets allow both simultaneous spatial and frequency analysis and scalable video processing. This thesis presents two new approaches. First, we propose new hardware architectures to address critical issues in 2-D dual-mode LDWT (supporting 5/3 lossless and 9/7 lossy coding modes). A considerably large transpose memory is the most critical requirement for LDWT. The proposed architecture can support high-resolution videos and reduce the internal memory requirement significantly. In our LDWT approach, the signal flow is revised from row-wise only to mixed row- and column-wise, and a new architecture, called interlaced read scan architecture (IRSA), is used to reduce the transpose memory. With the IRSA approach, the transpose memory size is only 2N or 4N (5/3 or 9/7 mode) for an N´N DWT. In addition, the proposed 2-D LDWT operates with parallel and pipelined schemes that increase its operation speed. It can be applied to real-time video operations for Motion-JPEG2000. Second, we propose the called symmetric mask-based DWT (SMDWT) for an intelligent video surveillance system. SMDWT improves the critical issue of the 2-D LDWT, and then obtains the benefit of low latency, reduced complexity, and low transpose memory. A highly precise and real-time moving object detection algorithm based on a low complexity SMDWT offers a mechanism for the sequential detection of both indoor (all day) and outdoor (all day) scenes. Computer simulations verified that the present algorithm performs well. It has high accuracy rate of more than 89.59% on average and the average frame rate can reach 47.1 frame per second (FPS).
  The abovementioned two algorithms for the LDWT were improved. The experimental results indicate that the proposed methods can provide low transpose memory and real-time processing for a high resolution intelligent video surveillance system.

CONTENTS

ACKNOWLEDGMENTS…………………………………………………………………I
中文摘要……………………………………………………………………………….III
ABSTRACT………………………………………………………………………………IV

CHAPTER 1: INTRODUCTION……………………………………………………..…1
1.1.	Motivation………………………………………………………………………...1 
1.1.1.	Advanced Challenges in Video Surveillance…………………………...3
1.1.2.	The Video Compression Domain……………………………………....7
1.1.3.	Motion-JPEG2000 in Surveillance……………………………………....9
1.1.4.	The Computer Vision Domain……………………………………......20
1.2.	Dissertation Organization……………………………………..............................22 

CHAPTER 2: THEORY AND BACKGROUND………………………………………23
	2.1. General Filter and Lifting-based………………………………………………...23 
		2.1.1. Lifting-based DWT Algorithm…………………………………………...27
	2.2. Lossless 5/3 Lifting-based Filter………………………………………………...29
2.3. Lossy 9/7 Lifting-based Filter…………………………………………………...30
2.4. Boundary Extension Treatment for Lifting-based Discrete Wavelet Transform...33

CHAPTER 3: DISCRETE WAVELET TRANSFORM IN MOTION-JPEG2000…35
	3.1. Survey of Lifting-based Discrete Wavelet Transform Hardware Architecture…36
	3.2. Proposed Interlaced Read Scan Algorithm…………………………………..…41
3.3. Proposed VLSI Architecture and Implementation…………………………..….51
	3.3.1. The First Stage 1-D LDWT…………………………………………..….52
	3.3.2. The Second Stage 1-D LDWT………………………………………..….57
3.3.3. 2-D LDWT Architecture………………………….…………………..….59
3.4. Experimental Results and Comparisons………………….…………………..….64
3.5. Summary...………………….…………………………………………….…..….74

CHAPTER 4: DISCRETE WAVELET TRANSFORM IN OBJECT DETECTION..76
4.1. The Proposed 2-D 5/3 Symmetric Mask-based Discrete Wavelet Transform...…76
	4.1.1. High-High Band………………….…………………………..…………..80
4.1.2. Low-Low Band………………….…………………………..………..…..85
4.1.3. High-Low Band………………….…………………………..………...…92
4.1.4. Low-High Band………………….…………………………..…………...98
4.1.5. Experimental Results and Performance Comparisons……..……….......102
4.1.6. Summary………………….…………………………..………………...113
4.2. Survey of Low Resolution for Object Detection..…………………………..….114
4.3. Discrete Wavelet Transform and Low Resolution Technology……………..….116
	4.3.1. Discrete Wavelet Transform Method………………….……………..….117
	4.3.2. Low Resolution Method………………….…………………………..…118
	4.4. Moving Object Detection and Tracking System.................................................120
		4.4.1. Direct LL-Mask Based Scheme...............................................................121
		4.4.2. Detection and Tracking Flow...................................................................122
		4.4.3. Occlusion Handling for Multiple Objects Tracking.................................126
	4.5. Experimental Results...........................................................................................129
		4.5.1. Dealing with Noise Issues........................................................................129 
4.5.2. Moving Object Tracking..........................................................................133
4.5.3. Multiple Moving Object Tracking and Occlusion....................................140
4.6. Summary..............................................................................................................145 

CHAPTER 5: CONCLUSIONS......................................................................................146
	5.1. Summary of Video Compression…………………............................................147
	5.1. Summary of Computer Vision……….................................................................148

Chapter 6: FUTURE WORKS........................................................................................150

REFERENCE...................................................................................................................153

APPENDIX.......................................................................................................................170
Biographical Sketch...................................................................................................170
Publication…..............................................................................................................171






LIST OF FIGURES

Figure 1.1 Anomaly detection and alarming………………………………………………..3
Figure 1.2 For example a land image: (a) low resolution, (b) low frame rate……………...4
Figure 1.3 Sample frames from the synopsis video: (a) the input video shows a walking person, and after a period of inactivity displays a flying bird. A compact video synopsis can be produced by playing the bird and the person simultaneously, (b) synopsis video demonstrates in Tamkang University…………………………..6
Figure 1.4 The CODEC for video surveillance system applications……………………….7
Figure 1.5 Scalability of video with Motion-JPEG2000 for DVR………………………….8
Figure 1.6 Multi-resolutions……………………………………….………………………11
Figure 1.7 The block diagram for JPEG2000 system: (a) system flow, (b) profile of JPEG2000 encoding timer……………………………………………………12
Figure 1.8 ROI: (a) the original image, (b) within ROI and zoom in…………………….18
Figure 1.9 PSNR for each standard performing lossy coding at low-bitrate 0.25, 0.5, 1, 1.5, and 2 bpp………………………………………………………………............18
Figure 1.10 Lena coded at low-bitrate: (a) 0.13 bbp by JPEG, (b) 0.13 bbp by JPEG2000……………………………………………………...……….......19
Figure 2.1 The 2-D analysis DWT image decomposition process………………………...25
Figure 2.2 3-level 1-D DWT decomposition using Mallat’s algorithm…………………...26
Figure 2.3 Block diagram of the lifting-based DWT……………………………………...28
Figure 2.4 Lifting-based 5/3 DWT algorithm……………………………………………..30
Figure 2.5 Lifting-based 9/7 DWT algorithm……………………………………………..32
Figure 2.6 DG of the 5/3 LDWT…………………………………………………………..34
Figure 3.1 2-D LDWT operation: (a) the flow of a traditional 2-D DWT, (b) detailed processing flow……………………………………………………………...40
Figure 3.2 The system block diagram of the proposed 2-D DWT: (a) 2-D dual-mode LDWT, (b) block diagram of the proposed system architecture, (c) one-level 2-D DWT architecture…………………………………………………….….43
Figure 3.3 Example of 2-D 5/3 mode LDWT operations………………………………….45
Figure 3.4 IRSA of the 2-D LDWT………………………………………………………..49
Figure 3.5 The detail operations of the first stage 1-D DWT………………………….…..50
Figure 3.6 The detailed operations of the second stage 1-D DWT: (a) the HF (HH and HL) part operations, (b) the LF (LH and LL) part operations……………………50
Figure 3.7 Block diagram of the row processor…………………………………………...52
Figure 3.8 The architecture of the first stage 1-D DWT…………………………………..53
Figure 3.9 The operation of the signal arrangement unit: (a) for example, IRAS signal in N1, (b) data flow is seen in Fig. 3.8…………………………………………...54
Figure 3.10 The hardware implementations of the four coefficients of the 9/7 filter: (a) α, (b) β, (c) γ, (d) δ……………………………………………………………..56
Figure 3.11 Combination of α and β for 5/3 and 9/7 filters: (a) modified α, (b) modified..56
Figure 3.12 Architecture of the column processor element: (a) the block diagram of the second stage 1-D LDWT. (b) the block diagram of the column processor…58
Figure 3.13 Signal merging process for the signal arrangement unit……………………...58
Figure 3.14 The input signal sequences: (a) IN1 read signal of even row in zig-zag orders, (b) IN2 read signal of odd row in zig-zag orders………………………….....60
Figure 3.15 The signal process of the two stage LDWT: (a) first stage 1-D LDWT, (b) second stage 1-D LDWT………………………………………….………….60
Figure 3.16 The complete 2-D DWT block diagram: (a) DSP diagram of the 2-D LDWT, (b) system diagram of the 2-D LDWT…………………………………………61
Figure 3.17 The processing procedures of 2-D dual-mode LDWTs under the same IRSA architecture…………………………………………………………………...62
Figure 3.18 The multi-level 2-D DWT architecture……………………………………….64
Figure 3.19 Rader plot for the comparison of 2input/2output 2-D LDWT hardware architectures. (a) Proposed v.s. [96]. (b) Proposed v.s. [97]. (c) Proposed v.s. [101]. (d) Proposed v.s. [102]………………………………………..……….72
Figure 4.1 The system block diagram of the proposed 2-D SMDWT: (a) 2-D 5/3 SMDWT, 
(b) De-relation matrix………………………………………………………….80
Figure 4.2 HH-band mask coefficients and the corresponding DSP architecture: (a) coefficients, (b) DSP architecture, (c) hardware architecture design………...81
Figure 4.3 LL-band mask coefficients and the corresponding DSP architecture: (a) coefficients, (b) DSP architecture, (c) hardware architecture design………...87
Figure 4.4 Repeat part (in gray) of the diagonal scanned position LL(1,1)……………….91
Figure 4.5 Repeat part (in gray) of the diagonal scanned position LL(2,2)……………….91
Figure 4.6 Repeat part (in gray) of the diagonal scanned position LL(3,3). ……………...91
  Figure 4.7 HL-band mask coefficients and the corresponding DSP architecture: (a)   coefficients, (b) DSP architecture, (c) hardware architecture design……….93
Figure 4.8 Repeat part (in gray) of the diagonal scanned position HL(1,1)………….…...97
Figure 4.9 Repeat part (in gray) of the diagonal scanned position HL(2,2)………….........97
Figure 4.10 LH-band mask coefficients and the corresponding DSP architecture: (a) coefficients, (b) DSP architecture, (c) hardware architecture design……….99
Figure 4.11 Repeat part (in gray) of the diagonal scanned position LH(1,1)…………….102
Figure 4.12 Repeat part (in gray) of the diagonal scanned position LH(2,2)…………….102
Figure 4.13 Schematic diagram of the 2-D SMDWT………………………………….107
Figure 4.14 PSNR (dB) versus Rate (bpp) comparison between 2-D LDWT and the proposed 2-D SMDWT…………………………………………………..107
Figure 4.15 DG of the 2-D LDWT critical path: (a) HH-band, (b) HL-band, (c) LH-band, (d) LL-band…………………………………………...…………………….108
Figure 4.16 The multi-level 2-D DWT architecture……………………………………...108
Figure 4.17 Diagrams of DWT image decomposition: (a) LL1 DWT, (b) LL2 DWT……118
Figure 4.18 Diagram of the 2×2 AFS………………………………………………….....119
Figure 4.19 Comparisons of low resolution images: (a) the original image (320×240), (b) each subband image with DWT from left to right as 160×120, 80×60, and 40×30, respectively, (c) each resolution image with the 2×2 AFS method from left to right as 160×120, 80×60, and 40×30, respectively…………………120
Figure 4.20 The subband mask coefficients of (a) HH, (b) HL, (c) LH, (d) LL…………122
Figure 4.21 The pre-processing flowchart of the moving object detection and tracking based on DLLBS……………………………………………………….….124
Figure 4.22 After most of the noises and fake motions are removed using SMDWT: (a) the original image, (b) LL2-band image……………………………………..….125
Figure 4.23 CPR flowchart……………………………………………………………….128
Figure 4.24 Moving object detection in the outdoor environment with fake motion: (a) the original image of three consecutive frames, (b) the temporal differencing results of the original image, (c) the temporal differencing results of the LL1-band image, (d) the temporal differencing results of the LL2-band image, (e) the temporal differencing results of the LL3-band image……………….131
Figure 4.25 Moving object detection in the indoor environment with Gaussian noise: (a) the original image of three consecutive frames, (b) the temporal differencing results of the original image, (c) the temporal differencing results of the LL1-band image, (d) the temporal differencing results of the LL2-band image, (e) the temporal differencing results of the LL3-band image……………….132
Figure 4.26 Examples of (a) successful moving object tracking, (b) failure moving object tracking……………………...…………………………………………….133
Figure 4.27 Test sequence 1 (320×240)…………………………………………………135
Figure 4.28 Test sequence 2 (320×240)………………………………………………….136
Figure 4.29 Test sequence 3 (320×240)…………………………………………………136
Figure 4.30 Test sequence 4 (320×240)…………………………………………………137
Figure 4.31 Test sequence 5 (320×240)………………………………………………….137
Figure 4.32 Test sequence 6 (320×240)………………………………………………….138
Figure 4.33 Test sequence 7 (320×240)………………………………………………….138
Figure 4.34 Test sequence 8 (320×240)………………………………………………….139
Figure 4.35 Test sequence 9 (320×240)………………………………………………….139
Figure 4.36 Test sequence 10 (320×240)………………………………………………...140
Figure 4.37 Test sequence 11 (320×240)…………………………………………………141
Figure 4.38 Test sequence 12 (320×240)………………………………………………...141
Figure 4.39 Test sequence 13 (320×240)………………………………………………...142
Figure 4.40 Test sequence 14 (640×480)………………………………………………...142
Figure 4.41 Test sequence 15 (640×480)………………………………………………...142
Figure 4.42 Test sequence 16 (640×480)……………………………………………...…143
Figure 5.1 The rest of this Ph.D. thesis is organized as follows…………………………146
Figure 6.1 Kim et al. proposes Motion-JPEG2000 coding scheme based on HVS for digital cinema: (a) block diagram of the proposed scheme, (b) motion detection process………………………………………………………………………..151























LIST OF TABLES

Table 1.1 Summary of the functionalities and characteristics of Motion-JPEG2000 with the other compression standard (within intra-mode)………………………………..13
Table 1.2 Compression standard comparisons…………………………………………….15
Table 1.3 Image quality level…………………………………………………...…………19
Table 2.1 Comparisons of the filter bank and lifting scheme……………………………...26
Table 2.2 Boundary extension to the left and to the right for JPEG2000…………………34
Table 3.1 Data flow of Fig. 3.8. (The FIFO latency is omitted here)……………………53
Table 3.2 Comparisons of the 2-D architectures for 5/3 LDWT…………………………..68
Table 3.3 Comparisons of the 2-D architectures for 9/7 LDWT…………………………..69
Table 3.4 Hardware cost and performance comparisons of high throughput 9/7 2-D DWT architectures…………………………….…………………………….…………70
Table 3.5 Design specification of the proposed 2-D LDWT………………………………73
Table 4.1 The subband mask for DSP……………………………………………………103
Table 4.2 HH-band wavelet coefficient (mask of size 3×3)……………………………...103
Table 4.3 HL-band wavelet coefficient (mask of size 5×3)……………………………104
Table 4.4 LH-band wavelet coefficient (mask of size 3×5)……………………………104
Table 4.5 LL-band wavelet coefficient (mask of size 5×5)………………………………105
Table 4.6 Performance comparisons…………………………………………………..…109
Table 4.7 Subband lifting-based v.s. mask-based for integer 2-D DWT…………………110
Table 4.8 Complexity comparisons among various 2-D DWT approaches……………..113
Table 4.9 Practical execution times of the LDWT and the proposed SMDWT approach.113
Table 4.10 The moving objects detection and tracking results…………………………..130
Table 4.11 The best threshold values, T, in different environments and DLLBS………..143
Table 4.12 Features of various methods………………………………………………….143
Table 4.13 Single moving object processing (without occlusion)………………………..144
Table 4.14 Multiple moving objects processing (with occlusion). ………………………144
Table 4.15 Multiple moving objects processing (with occlusion). ………………………145

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