移動物件偵測的背景移除方法之比較研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：13

、訪客IP：18.217.230.80

姓名

盧美榮(Mei-jung Lu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

移動物件偵測的背景移除方法之比較研究
(Comparative Study of Background Subtraction Methods for Moving Object Detection Application)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

典型的視訊監控目的，是將影像中感興趣的物件擷取出來作分析、辨識等應用。而物件擷取技術則通常建立在背景移除法的基礎上。本論文以一個公開的視訊資料庫來評估7種主要的背景移除法：FD、AMF、Stauffer GMM、Zivkovic GMM、KDE、Eigenbackground、Codebook，我們採用ROC曲線作為背景移除性能評估方法。有別於前人在背景移除法領域中所做的檢視(survey)項目，本論文特別針對了這些演算法在嵌入式硬體化實現的適合性進行全面探討，我們比較每個演算法的執行效能(速度)、記憶體使用量、程式碼大小，以利作為後續從事背景移除法的嵌入式硬體實作之重要參考。從實驗的結果顯示出，近似中值濾波法幾乎在各種影片分類中，準確度與精確度的表現皆為前兩名，再加上其演算法簡單、記憶體使用量較少，對於移動物件偵測的嵌入式系統設計是個不錯的選擇。此外這樣的成果也間接證實了背景移除法的難易程度與準確度及精確度的高低並沒有關聯性。

摘要(英)

Video surveillance is typically used to capture moving subjects which users may be interested in from an image sequence, then applying it into different software applications for analysis and identification afterwards. These kinds of techniques are usually based on background subtraction (BS). In this paper, seven popular BS algorithms (FD, AMF, Stauffer GMM, Zivkovic GMM, KDE, Eigenbackground, Codebook) are compared and evaluated with open source video database to rate their performance. We adopted the receiver operating characteristic (ROC) curve as the evaluation metric to compare the result of BS algorithm, foreground object, with accurate ground truth data. In contrast with previous BS studies, this paper is especially focused on the complexity of implementing these methods on hardware device, like FPGA. Several properties of each algorithm will also be discussed in the article including accuracy, precision, efficiency, memory usage, and code size. The findings will provide reference for future BS algorithm applications on embedded hardware systems. Our results show that approximated median filtering is precise and performs superior in every evaluative category. Considering its ease of use and minimal memory requirements, it is a pragmatic choice for embedded systems design. Furthermore, our findings reveal no significant difference between accuracy of the results from the various BS methods used.

關鍵字(中)

★ 背景移除法
★ 背景建模
★ 視訊監控
★ 近似中值濾波法
★ 高斯混合模型

關鍵字(英)

論文目次

目錄
摘要 i
Abstract ii
謝誌 iii
目錄 v
圖目錄 ix
表目錄 xi
第一章緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 4
第二章相關文獻回顧 5
2.1 背景移除(Background Subtraction) 5
2.2 影像差異法(Frame Difference) 7
2.3 近似中值濾波法(Approximated Median Filtering) 7
2.4 單高斯背景模型(Single Gaussian Background Model) 8
2.5 高斯混合模型(Gaussian Mixture Model) 9
2.6 非參數核密度估計(Non-parameter Kernel Density Estimation) 10
2.7 特徵背景模型(Eigenbackground Model) 11
2.8 碼本背景模型(Codebook Background Model) 11
2.8.1 Codebook初始建立[17][23] 13
2.8.2 Codebook建模演算法 16
2.8.3 Codebook背景切割演算法 17
第三章背景移除方法的性能評估 18
3.1 背景移除方法原理 18
3.2 驗證資料庫 18
3.3 性能評估方法 29
3.3.1 Ground truth 29
3.3.2 ROC(Receiver Operating Characteristic curve)曲線 30
3.3.3 性能(performance) 32
3.3.3.1 準確度(accuracy)與精確度(precision) 32
3.3.3.2 效能(efficiency) 32
3.3.3.3 記憶體使用量(memory usage) 33
3.3.3.4 程式碼大小(code size) 33
3.3.3.5 即時性(real time) 33
3.3.3.6 適合硬體化(hardware friendly) 33
第四章實驗與比較 35
4.1 實驗相關軟硬體介紹 35
4.1.1 實驗設備平台與開發環境 35
4.1.2 BGSLibrary介紹 36
4.1.3 系統設計流程圖(IDEF0架構) 37
4.2 前景影像與ground truth比較 38
4.3 性能比較 57
4.3.1 時間 57
4.3.2 記憶體使用量 59
4.3.3 程式碼大小 61
4.4 適合硬體化程度 62
4.4.1 疊代與非疊代 63
4.4.2 各演算法Grafcet流程圖 64
4.4.2.1 影像差異法(Frame Difference) 65
4.4.2.2 近似中值濾波法(Approximated Median Filtering) 66
4.4.2.3 高斯混合模型法(Gaussian Mixture Model) 67
4.4.2.4 核密度估計法(Kernel Density Estimation) 69
4.4.2.5 特徵背景模型法(Eigenbackground Model) 71
4.4.2.6 碼本背景模型法(Codebook Background Model) 72
4.4.3 統計型(statistical)演算法的變數含意說明 73
4.5 綜合比較結果 74
第五章結論與未來展望 75
5.1 結論 75
5.2 未來展望 76
參考文獻 78

參考文獻

1 參考文獻
[1] R. Jain and H. H. Nagel, “On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, pp.206-214, Apr. 1979
[2] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-Time Tracking of the Human Body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, Jul. 1997
[3] A. M. Mc Ivor, “Background subtraction techniques,” International Conference on Image and Vision Computing, New Zealand, IVCNZ, 2000
[4] M. Piccardi, “Background subtraction techniques: a review,” IEEE International Conference on Systems, Man and Cybernetics, vol.4, pp. 3099-3104, 10-13 Oct. 2004
[5] S. Cheung and C. Kamath, “Robust background subtraction with foreground validation for urban traffic video,” EURASIP Journal on Applied Signal Processing, vol.14, pp.2330-2340, 2005
[6] S. Elhabian, K. El-Sayed, S. Ahmed, “Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art,” Recent Patents on Computer Science, vol. 1, no. 1, pp 32-54, Jan. 2008.
[7] D. H. Parks and S. S. Fels, “Evaluation of Background Subtraction Algorithms with Post-processing,” IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp.192-199, 1-3 Sep. 2008
[8] M. Cristani, M. Farenzena, D. Bloisi, and V. Murino, “Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review,” EURASIP Journal on Advances in Signal Processing, 24 pages, vol. 2010, Aug. 2010.
[9] T. Bouwmans, F. El Baf, and B. Vachon, “Statistical Background Modeling for Foreground Detection: A Survey”, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, vol. 4, Part 2, Chapter 3, pp. 181-199, Jan. 2010
[10] T. Bouwmans, “Subspace Learning for Background Modeling: A Survey”, Recent Patents on Computer Science, vol. 2, no. 3, pp. 223-234, Nov. 2009.
[11] T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014.
[12] S. Brutzer, B. H¨oferlin, G. Heidemann, “Evaluation of Background Subtraction Techniques for Video Surveillance,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1937-1944, 20-25 Jun. 2011
[13] S.Jeeva and M.Sivabalakrishnan, “Survey on Background Modeling and Foreground Detection for Real Time Video Surveillance,” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15),vol.50, pp. 566 – 571, 2015
[14] C. Stauffer and W.E.L Grimson, “Adaptive background mixture models for real-time tracking,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, pp.246-252, Jun. 1999
[15] A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, ECCV 2000, pp. 751-767, Dublin, Ireland, Jun. 2000.
[16] A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance," Proceedings of the IEEE, vol. 90, no.7, pp. 1151-1163, Jul. 2002.
[17] N. M. Oliver, B. Rosario, and A. P. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions,” IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 22, no. 8, Aug. 2000
[18] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time foreground-background segmentation using codebook model," Real-Time Imaging, vol. 11, no.3, pp. 172-185, 2005
[19] G. Deng, K. Guo, “Self-adaptive background modeling research based on change detection and area training,” IEEE Workshop on Electronics, Computer and Applications, pp. 59-62, 2014
[20] N. J. B. Mcfarlane and C. P. Schofield, "Segmentation and Tracking of Piglets in Images," Machine Vision and Applications, vol. 8, pp. 187-193, 1995.
[21] Creason (2012,Aug 18) 单高斯与混合高斯模型(Gaussian Model and Mixture Gaussian Model) Available: http://creason.sourceforge.net/mixture-gaussian/
[22] A. Elgammal, D. Harwood, and L. Davis, “Non-parametric Model for Background Subtraction,” 6th European Conference on Computer Vision Lecture Notes in Computer Science, vol.1843, pp.751-767, 18 Apr. 2003
[23] 劉乃菀, “硬體化動態物件偵測引擎設計與手勢辨識應用”，國立中央大學資訊工程學系碩士論文，2013.
[24] A. Sobral, and A. Vacavant, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,” Elsevier Computer Vision and Image Understanding, vol.122, pp.4-21, May 2014
[25] N. Goyette, P. M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar, “changedetection.net: A New Change Detection Benchmark Dataset,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.1-8, 16-21 Jun. 2012
[26] Y. Wang, P. M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, “CDnet 2014: An Expanded Change Detection Benchmark Dataset” IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.393-400, 23-28 Jun. 2014
[27] A. Vacavant, T. Chateau, A. Wilhelm and L. Lequièvre, “A Benchmark Dataset for Foreground/Background Extraction,” ACCV 2012 Workshop: Background Models Challenge, vol. 7728, pp.291-300, Nov. 2012, Daejeon, Korea.
[28] A. Sobral and A. Vacavant, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,” Computer Vision and Image Understanding, vol. 122, pp.4-21, 2014.
[29] A. Vacavant, L. Tougne, T. Chateau and L. Robinault, “Evaluation of Background Models with Synthetic and Real Data,” In Background Modeling and Foreground Detection for Video Surveillance, Thierry Bouwmans, Fatih Porikli, Benjamin Hörferlin and Antoine Vacavant, Chapman and Hall/CRC, 2014.
[30] A. Vacavant, L. Tougne, L. Robinault and T. Chateau, “CVIU, Special Section on Background Models Comparison,” Computer Vision and Image Understanding Elsevier, vol. 122, pp. 1-3, May 2014.
[31] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallﬂower: Principles and practice of background maintenance,” In Proc. IEEE Int.Conf. Computer Vision, vol. 1, pp. 255–261, 1999.
[32] D. Young and J. Ferryman, “PETS metrics: Online performance evaluation service,” In Proc. IEEE Int. Workshop on Performance Evaluation of Tracking Systems, pp. 317–324, 2005.
[33] R. Vezzani and R. Cucchiara, “Video surveillance online repository(visor): an integrated framework,” Multimedia Tools and Applications, 50(2):359–380, 2010.
[34] L. Li, W. Huang, I. G. Yu-Hua, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans.Image Process, 13(11):1459-1472, 2004.
[35] M. Karaman, L. Goldmann, D. Yu, and T. Sikora, “Comparison of static background segmentation methods,” In Proc. SPIE Visual Communications and Image Process, pp. 2140-2151, 2005.
F. Tiburzi, M. Escudero, J. Bescos, and J. Martinez, “A ground truth for motion-based video-object segmentation,” In Proc. IEEE Int. Conf. Image Processing, pp. 17–20, 2008.

指導教授

陳慶瀚(Ching-han Chen)

審核日期

2016-5-3

推文