博碩士論文 103521064 詳細資訊




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姓名 李政勳(Zheng-Xun Li)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 深度與色彩資訊於PMHPSO-TVAC之即時多目標追蹤應用
(Object Tracking Based On PMHPSO-TVAC with Color and Depth Data in Real Time)
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摘要(中) 近年來,隨著攝影機與監視器的普及,物件偵測及物件追蹤是電腦視覺領域中一門重要且充滿挑戰性的研究課題。針對單一物件追蹤而言,其困難點在於複雜環境與追蹤物件的高變異性,複雜環境因素包含:光線、角度,而追蹤物外觀的變異性又可分為剛體(角度變化)與非剛體形變,以及遮蔽等問題。
本研究中,主要應用改良型PSO演算法(PMHPSO-TVAC)來對目標物進行即時追蹤。而在偵測目標物方面,則以影像相減法來切割出目標物與背景。再來則是利用改良的種子區域生長法來標記各個目標物,區分出各個目標物後,再計算出各個目標物的中心位置。接著對各個目標物建構顏色直方圖與深度直方圖模型以便做追蹤使用,然而在追蹤過程中很容易受到光線變化影響,採用HSV色彩空間中的色相,盡量減少了亮度的影響。然而,在光線極度昏暗的情況下能無法改善干擾,故本論文建構目標物的深度直方圖模型來補償目標物的描述方式。
最後,利用目標物的深度直方圖與顏色直方圖模型,以PMHPSO-TVAC演算法來進行多目標追蹤。
摘要(英) In recent years, with the popularity of the camera and monitor, object detection and object tracking field are important and challenging research topic. For object tracking, it is difficult to track objects in complex environments. In order to improve the tracking speed and solve the shadowing problem, this paper uses Position Mutated Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PMHPSO-TVAC) algorithm for object tracking in real time. In terms of object detection, in this study, the background subtraction is used. And can cut out complete targets. The background subtraction has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target. Then, for model building, color histograms are used to build target models. However, in no-light environment, we can’t track any target, so this paper construct depth histogram object model to compensate for object model. Finally, we used the depth histogram and color histogram model with PMHPSO-TVAC algorithms for multi-target tracking.
關鍵字(中) ★ PMHPSO-TVAC演算法
★ 目標物追蹤
★ 目標物偵測
★ 種子區域生長法
★ 深度資料
關鍵字(英) ★ PMHPSO-TVAC
★ Object tracking
★ Object detection
★ Depth data
★ Seed Region Growing Method
論文目次 中文摘要 V
Abstract VI
誌謝 VII
目錄 VIII
圖目錄 X
第一章 緒論 1
1-1文獻回顧與探討 1
1-2研究動機與方法 2
1-3成果與主要貢獻 3
1-4論文結構 3
第二章 軟硬體設備與環境 5
2-1 硬體設備與環境 5
2-2 軟體設備與環境 6
2-3 系統架構 7
第三章 目標物外觀模型 8
3-1 色彩空間 9
3-1-1 YCbCr色彩空間 9
3-1-2 RGB色彩空間 10
3-1-3 HSV色彩空間 11
3-1-4 RGB轉HSV 11
3-1-5 RGB轉灰階 12
3-1-6深度影像轉灰階 13
3-2 目標物偵測 13
3-2-1 LK光流法(Optical flow) 13
3-2-2 卡爾曼濾波器(Kalman filter) 14
3-2-4 影像相減法(Background substraction) 14
3-3 形態學處理 15
3-3-1膨脹(Dilation) 15
3-3-2侵蝕(Erosion) 16
3-3-3斷開(Opening) 17
3-3-4閉合(Closing) 18
3-4 影像區塊分割 19
3-4-1區域分割合併法(Region Splitting and Merging) 20
3-4-2種子區域生長法(Seeded Region Growing) 21
3-4-3種子區域生長法改良 23
3-5 目標物模型 26
第四章 演算法追蹤 28
4-1 粒子群演算法(Particle Swarm Optimization Algorithm) 28
4-2 改良粒子群演算法(Improved Particle Swarm Optimization) 32
4-3解空間與搜尋範圍 39
4-3-1 解空間 39
4-3-2 搜尋範圍 39
4-4適應函數分析 40
4-5遮蔽問題與改善方法 41
第五章 目標物模型更新 45
第六章 實驗結果與討論 49
6-1 模擬實驗 49
6-2 實際實驗 57
第七章 結論建議與未來展望 74
7-1 結論 74
7-2 建議 74
7-3 未來展望 75
參考文獻 76
參考文獻 [1] M. Kilger,“A Shadow Handler in a Video-based Real-time Traffic Monitoring System,” Applications of Computer Vision, Proceedings, 1992., IEEE Workshop on 1992
[2] T.Intachak and W.Kaewapichai ,“Real-Time Illumination Feedback System for Adaptive Background Subtraction Working in Traffic Video Monitoring”, International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) December 7-9,2011
[3] W.Hu and X.Zhou,” Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 5, MAY 2013
[4] P.Viola ,“ Detecting Pedestrians Using Patterns of Motion and Appearance”, Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV’03) 2003
[5] D.M. Gavrila and L.S. Davis,” 3-D model-based tracking of humans in action: a multi-view approach”, Computer Vision and Pattern Recognition, 1996. Proceedings CVPR ′96, 1996 IEEE Computer Society Conference 1996.
[6] F.Torres , “INITIALIZING 3D MODEL-BASED TRACKING OF DICE”, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP) 2012
[7] B.Castaneda and Y.Luzanov,” A Modular Architecture For Real–Time Feature-Based Tracking”, Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP ′04). IEEE International Conference on 2004
[8] J. Kennedy and R. C. Eberhert, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, vol. 4 pp. 1942-1948, 1995.
[9] Jong-Bae Park, Yun-Won Jeong , Joong-Rin Shin and Kwang Y. Lee “
An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems”,IEEE Transactions On Power Systems, Vol. 25, No. 1, February 2010.
[10] Y. Zheng and Y. Meng, “Adaptive Object Tracking using Particle Swarm Optimization,” International Symposium on Computational Intelligence in Robotics and Automation, pp. 43-48, 2007.
[11] X. Zhang, W. Hu, S. Maybank, X. Li, and M. Zhu, “Sequential Particle Swarm Optimization for Visual Tracking,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[12] T. Kobayashi, K. Nakagawa, J. Imae and G. Zhai, “Real Time Object tracking on Video Image Sequence using Particle Swarm Optimization,” International Conference on Control, Automation and Systems, pp. 1773-1778, 2007.
[13] 李維平 王雅賢 江正文, “粒子群最佳化演算法改良之研究”’ Journal of Science and Engineering Technology, Vol. 4, No. 2, pp. 51-62 (2008)
[14] Yuhui Shi and Russell Eberhart,” A Modified Particle Swarm Optimizer”Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., IEEE International Conference.1998
[15] Asanga Ratnaweera, Saman K. Halgamuge and Harry C. Watson “Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 8, NO. 3, JUNE 2004
[16] H. Wang, M. W. Ren, and J. Y. Yang, “Object Tracking based on Genetic Algorithm and Kalman filter,” International Conference on Computational Intelligence and Security, vol. 1, pp. 80–85, 1997.
[17] X. Dong, J. Cao, H. Yang, Z. Yu, H. Guo and C. Liu, “Object Tracking based on integrating the Genetic algorithm with complex method,” International Conference on Intelligent Control and Information Processing, pp. 205-209, 2013.
[18] Boris Babenko, Ming-Hsuan Yang, and Serge Belongie,” Robust Object Tracking with Online Multiple Instance Learning” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 33, NO. 8, AUGUST 2011
[19] 黃雅軒,張倞禕,黃育甫,” Face Tracking With Online Multiple Instance Learning”, 第十三屆離島資訊技術與應用研討會論文集
[20] 林毓航,「整合紋理與深度線索之高準確性物件追蹤系統」,國立高雄應用科技大學,碩士論文,民國103年
[21] L. Bischof and R. Adams, “Seeded Region Growing,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647, June 1994.
[22] R.Bhattacharya, T.K.Bhattacharyya and Ramesh Garg,” Position Mutated Hierarchical Particle Swarm Optimization and its Application in Synthesis of Unequally Spaced Antenna Arrays ”, IEEE Transactions On Antennas And Propagation, Vol. 60, No. 7, 2012
[23] A. Djouadi, O. Snorrason and F. D. Garber, “The Quality of Training-Sample Estimates of the Bhattacharyya Coefficient”, IEEE Transactions Pattern Analysis Machine Intelligence, Vol. 12, pp. 92-97, 1990.
[24] T. Kailath, “The Divergence and Bhattacharyya Distance Measures in Signal Selection”, IEEE Transactions Communication Technology, Vol. COM-15, pp. 52-60, 1967.
[25] J. L. Barron, D. J. Fleet, S. S. Beauchemin and T. A. Burkitt, “Performance of optical flow techniques,” International Journal of Computer Vision, pp. 43-77, 1994.
[26] M. Mueller, P. Karasev, I. Kolesov and A. Tannenbaum, “Optical Flow Estimation for Flame Detection in Videos,” IEEE Transactions on Image Processing, vol. 22, pp. 2786-2797, 2013.
[27] 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, 1997.
[28] L. Maddalena and A. Petrosino, “A Self-Organizing Approach to Background Substraction for Visual Surveillance Applications,” IEEE Transactions on Image Processing, vol.17, pp. 1168-1177, 2008.
[29] 張榮貴,「基於HPSO-TVAC演算法於多目標追蹤系統之研究」,國立中央大學,碩士論文,民國103年。
[30] 蔡尚麟,「基於人工蜂群演算法之物件追蹤研究」,國立中央大學,碩士論文,民國104年。
指導教授 鍾鴻源(Hung-Yuan Chung) 審核日期 2016-7-15
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