以SoPC軟硬體整合方式實現之智慧型監控系統

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：111

、訪客IP：18.119.107.96

姓名

張志豪(Chih-Hao Chang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以SoPC軟硬體整合方式實現之智慧型監控系統
(Design for an Intelligent Surveillance System based on System-on-a-Programmable-Chip Platform)

相關論文

★ 即時的SIFT特徵點擷取之低記憶體硬體設計	★ 即時的人臉偵測與人臉辨識之門禁系統
★ 具即時自動跟隨功能之自走車	★ 應用於多導程心電訊號之無損壓縮演算法與實現
★ 離線自定義語音語者喚醒詞系統與嵌入式開發實現	★ 晶圓圖缺陷分類與嵌入式系統實現
★ 語音密集連接卷積網路應用於小尺寸關鍵詞偵測	★ G2LGAN: 對不平衡資料集進行資料擴增應用於晶圓圖缺陷分類
★ 補償無乘法數位濾波器有限精準度之演算法設計技巧	★ 可規劃式維特比解碼器之設計與實現
★ 以擴展基本角度CORDIC為基礎之低成本向量旋轉器矽智產設計	★ JPEG2000靜態影像編碼系統之分析與架構設計
★ 適用於通訊系統之低功率渦輪碼解碼器	★ 應用於多媒體通訊之平台式設計
★ 適用MPEG 編碼器之數位浮水印系統設計與實現	★ 適用於視訊錯誤隱藏之演算法開發及其資料重複使用考量

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)

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tracking的效果一直是個挑戰，本論文提出一個以Andes Core為基礎的系統晶片設計，主要包含Foreground detection及Connected component labeling硬體加速模組和一個Processor 的System-on-a-Programmable-Chip 智慧監控系統，利用處理器可將所取得的前景資訊做object tracking以及data compression等運算，考慮到攝影機所搭載的處理器能力，本論文提出一個低複雜度物件追蹤演算法，主要以物件外型及中心點特徵來達到連續畫面物件追蹤的能力，即使物件交會或是物件被遮蔽住亦能判斷其位置，達到追蹤的效果，此外更提出object grouping的演算法來解決當物件紋路與背景太過相近而造成的物件破碎問題，Run-length coding 壓縮技術不僅減少labeling的運算時間，更達到了平均98%的壓縮率.

摘要(英)

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 a hardware design for the intelligent surveillance system. We have a complete system-level solution on algorithm and VLSI implementation. We evaluate the behavior of the moving objects with adaptive search method. We provide the method to track the moving people in successive frame by object boundary box and velocity without color cues or appearance model. Even though people are interacted with each other or the occlusion is caused by other foreground objects, the proposed algorithm can still perform well. Furthermore we consider the distance with camera as an adaptive search range to deal with the people movement issue. As the foreground is similar to the background, the proposed algorithm can still detect the object well. We also propose an embedded data compression technique which not only reduces the computational complexity but also achieves high compression rate. The overall system is developed in a platform-based system-on-a-Programmable-Chip (SOPC) as a demonstration result. As a VLSI implementation result, the logical gate count occupies 139.890 K and the throughput of foreground detection is 6403 K pixels.

關鍵字(中)

★ 監控系統
★ 前景切割
★ 物件追蹤＿
★ 連通物件標記

關鍵字(英)

論文目次

摘要....................................................i
Abstract...............................................ii
Table of Contents......................................iv
List of Figures........................................vi
List of Tables.......................................viii
Chapter 1 Introduction.................................1
1.1 Motivation......................................1
1.2 Foreground Segmentation Overview................7
1.3 Object Tracking Overview........................5
1.4 Thesis Organization.............................7
Chapter 2 Overall of the Proposed Intelligent Surveillance System.....................................8
2.1 Foreground Detection...........................10
2.1.1. Algorithm of Multi Background Maintain.........10
2.1.2. MBM Hardware Accelerator.......................12
2.2 Connected Component Labeling...................14
2.2.1. Algorithm of Object Labeling...................14
2.2.2. Object Labeling Hardware Accelerator...........17
2.3 Object Grouping................................19
2.3 Object Tracking................................20
2.3 Data Compression...............................27
Chapter 3 Architecture Design for Intelligent Surveillance System....................................28
3.1 The Proposed SOPC Architecture.................28
3.2 The Schedule of Proposed System................30
Chapter 4 Implementations and Experimental Results .......................................................33
4.1 Result of Foreground Detection with Connected Component Labeling.....................................33
4.2 Result of Object Tracking......................35
4.3 Result of Data Compression.....................38
4.4 Chip Specification.............................40
4.5 Implementations on SOPC Platform...............43
Chapter 5 Conclusion..................................45
References.............................................47

參考文獻

[1] M. Valera and S. Velastin, “Intelligent distributed surveillance systems: A review,” IEE Proc. Vis. Image, Signal Process., vol. 152, no. 2, pp. 192–204, Apr. 2005.
[2] M. Quaritsch, M. Kreuzthaler, B. Rinner, H. Bischof, and B. Strobl, “Autonomous multicamera tracking on embedded smart cameras,” EURASIP J. Embed. Syst., vol. 2007, pp. 1–10, Jan. 2007.
[3] S. Fleck and W. Straber, “Smart camera based monitoring system and its application to assisted living,” Proc. IEEE, vol. 96, no. 10, pp. 1698– 1714, Oct. 2008.
[4] J.-W. Hsieh, Y.-T. Hsu, H.-Y. M. Liao, and C.-C. Chen, “Video based human movement analysis and its application to surveillance systems,” IEEE Trans. Multimedia, vol. 10, no. 3, pp. 372–384, Apr.
[5] Fei Zhang; Pinyi Ren; Hao Chen; Guobing Li, ”Embedded intelligent video surveillance and cooperative tracking system,” Communications and Networking in China (CHINACOM), 2012 7th International ICST Conference on , vol., no., pp.632,637, 8-10 Aug. 2012
[6] R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image changedetection algorithms: A systematic survey,” IEEE Trans. Image Process.,vol. 14, no. 3, pp. 294–307, Mar. 2005.
[7] D. A. Migliore, M. Matteucci, and M. Naccari, “View-based detectionand analysis of periodic motion,” in Proc. 4th ACM Int. Workshop Video Surveill. Sensor Netw., Oct. 2006, pp. 215–218.
[8] 2008. M. Shah and S. Khan. “Tracking multiple occluding people by localizing on multiple scene planes.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 31–3:505–519, 2009.
[9] L. Li and M. Leung, “Integrating intensity and texture differences forrobust change detection,” IEEE Trans. Image Process., vol. 11, no. 2,pp. 105–112, Feb. 2002
[10] T. Morimoto, H. Adachi, K. Yamaoka, K. Awane, T. Koide, and H. J. Mattausch, “An FPGA-based region-growing video segmentation system with boundary-scan-only LSI architecture,” in Proc. Conf. IEEE APCCAS, Dec. 2006, pp. 944–947.
[11] J. Kim and T. Chen, “A VLSI architecture for video-object segmentation,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 1, pp. 83–96, Jan. 2003.
[12] W.-K. Chan, J.-Y. Chang, T.-W. Chen, and S.-Y. Chien, “Efficient content analysis engine for visual surveillance network,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 5, pp. 693–703, May 2009.
[13] D.-Z. Peng et al., “Architecture Design For A Low-Cost and Low-Complexity Foreground Object Segmentation with Multi-model Background Maintenance Algorithm,” in Proc. of IEEE Int. Conf. Image Process., Cairo, Egypt, Nov. 2009.
[14] Yilmaz, A., Javed, O., and Shah, M. 2006. “Object tracking: A survey”. ACM Comput. Surv. 38, 4, Article 13,December 2006
[15] M. Shah and S. Khan. “Tracking multiple occluding people by localizing on multiple scene planes.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 31–3:505–519, 2009.
[16] P. Kumar, S. Ranganath, K. Sengupta, and H. Weimin, “Cooperative Multitarget tracking with efficient split and merge Handling”, IEEE Trans. Circuits and systems for video technology, vol. 16, no. 12, December 2006.
[17] A. Senior, A. Hampapur, Y. Tian, L. Brown, S. Pankanti, and R. Bolle.” Appearance models for occlusions handling. “In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2001.
[18] A. Elgammal and L. Davis. “Probabilistic framework for segmenting people under occlusion.” In IEEE International Conference on Computer Vision, 2001.
[19] T. Zhao and R. Nevatia. “Tracking multiple humans in complex situations.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26–9:1208–1221, 2004.
[20] A. Amer, “Voting-based simultaneous tracking of multi-ple video objects”, IEEE Trans. Circuits and systems for video technology, vol. 15, no. 11, November 2005
[21] I. Haritaoglu, D. Harwood, and L. S. Davis,” W4: real-time surveillance of people and their activities” IEEE Trans. Pattern Anal Machine intell., vol. 22, no. 8, August 2000
[22] Conte, D.; Foggia, P.; Percannella, G.; Vento, M., ”Performance Evaluation of a People Tracking System on PETS2009 Database,” Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on , vol., no., pp.119,126, Aug. 29 2010-Sept. 1 2010
[23] T.-H. Tsai, W.-T. Sheu and C.-Y. Lin, ”Foreground Object Detection based on Multi-model Background Maintenance,” IEEE International Symposium on Multimedia, Taiwan, 2007.
[24] C.-Y. Lin, S.-Y. Li and T.-H. Tsai, “A scalable parallel hardware architecture for connected component labeling,” IEEE ICIP., Sept. 2010.
[25] Andes Technology, http://www.andestech.com/
[26] PETS2010“, In Conjunction with IEEE Computer Society (PAMI TC) and IEEE Signal Processing Society (IVMSP TC) Boston, US - 29th August 2010
[27] W.-K. Chan et al., “Efficient Content Analysis Engine for Visual Surveillance Network,” IEEE Trans. Circuit Syst. Video Technol., vol. 19, pp. 693 – 703, May. 2009.

指導教授

蔡宗漢(Tsung-Han Tsai)

審核日期

2014-12-2

推文