基于DM6467的目标分割与识别系统
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摘要
复杂背景下的目标分割和识别一直是航空航天等相关领域研究的重要内容。本文首先对复杂背景下目标检测、跟踪和识别(DTR)系统中的空间目标分割算法进行了改进,并基于嵌入式开发平台和软件系统架构的特点对DTR系统的实际运行效率进行了优化,最后对空间目标检测、跟踪和识别硬件运行平台各模块电路进行了初步的设计。主要内容如下:
1)针对由卫星仿真工具包STK产生的空间目标图像序列,提出一种基于改进先验形状CV模型的目标分割算法。该算法基于先验形状约束的变分水平集模型,在Chan模型的基础上,加入剪切及X、Y方向拉伸不变两种特性,拓展了先验形状对目标的自适应性。该算法可以较好地克服图像中出现的遮挡、阴影、噪声等干扰的影响,对于复杂背景下姿态变换较大的目标也有良好的分割效果。
2)基于DaVinci系统架构的特点并利用SoC analyzer工具,对DTR系统进行优化。优化后的DTR系统运行效率有了较大的提升,基本满足系统的实时处理需求。
3)针对DTR系统各算法模块的特点,基于TMS320DM6467处理器和各个片上的外设模块完成了板上系统的整体电路设计。所设计的硬件电路体现了DTR系统的特点。
Detecting, tracking and recognizing (DTR) spatial objects under the cluttered background have always been an important research area of aerospace exploration. In this paper, an improved image segmentation algorithm in DTR system is firstly introduced; Based on embedded system platform and system developing architecture, DTR system’performing efficiency was then improved; Finally, a hardware platform for DTR system is redesigned. Main research results are listed as follows:
1) For target image sequence generated by Satellite Tool Kits, an object segmentation algorithm based on improved prior shape and CV model is given. Given model in this paper is based on variational level set model, which introduces another two local adaptive transformation in Chan-Zhu model. New improved model in this paper overcomes not only interferences such as occlusion, shadow, noise, clutter, but also better segmentation result towards objects with complex affine transformation. In addition to that, improved model in this paper can give a better segmentation result than Chan-Zhu model. Disturbed by light and angel, locations of the building towards in Aerial remote sensing images are often uncertain. To extract interest object in such images, Chan-Zhu model will be restricted in limited self-adapting transformation towards shape prior, yet improved model in this model will have no such worry. In collusion, improved model in this paper has a better robustness.
2) With the help of SEED-DVS6467 development tool chain and SoC analyzer, DTR system’performing efficiency on SEED-DVS6467 was optimized. Running speed of optimized system was improved greatly, and optimized system on platform basically satisfied our requirements.
3) According to the analysis result of modules distribution in the DTR system, redesign the architecture of hardware platform, which is based on TMS320DM6467 processor and corresponding Peripherals such as video switching chips, DDR2 SDRAM, NAND flash, etc. Designed platform highlights the features of DTR system.
1)针对由卫星仿真工具包STK产生的空间目标图像序列,提出一种基于改进先验形状CV模型的目标分割算法。该算法基于先验形状约束的变分水平集模型,在Chan模型的基础上,加入剪切及X、Y方向拉伸不变两种特性,拓展了先验形状对目标的自适应性。该算法可以较好地克服图像中出现的遮挡、阴影、噪声等干扰的影响,对于复杂背景下姿态变换较大的目标也有良好的分割效果。
2)基于DaVinci系统架构的特点并利用SoC analyzer工具,对DTR系统进行优化。优化后的DTR系统运行效率有了较大的提升,基本满足系统的实时处理需求。
3)针对DTR系统各算法模块的特点,基于TMS320DM6467处理器和各个片上的外设模块完成了板上系统的整体电路设计。所设计的硬件电路体现了DTR系统的特点。
Detecting, tracking and recognizing (DTR) spatial objects under the cluttered background have always been an important research area of aerospace exploration. In this paper, an improved image segmentation algorithm in DTR system is firstly introduced; Based on embedded system platform and system developing architecture, DTR system’performing efficiency was then improved; Finally, a hardware platform for DTR system is redesigned. Main research results are listed as follows:
1) For target image sequence generated by Satellite Tool Kits, an object segmentation algorithm based on improved prior shape and CV model is given. Given model in this paper is based on variational level set model, which introduces another two local adaptive transformation in Chan-Zhu model. New improved model in this paper overcomes not only interferences such as occlusion, shadow, noise, clutter, but also better segmentation result towards objects with complex affine transformation. In addition to that, improved model in this paper can give a better segmentation result than Chan-Zhu model. Disturbed by light and angel, locations of the building towards in Aerial remote sensing images are often uncertain. To extract interest object in such images, Chan-Zhu model will be restricted in limited self-adapting transformation towards shape prior, yet improved model in this model will have no such worry. In collusion, improved model in this paper has a better robustness.
2) With the help of SEED-DVS6467 development tool chain and SoC analyzer, DTR system’performing efficiency on SEED-DVS6467 was optimized. Running speed of optimized system was improved greatly, and optimized system on platform basically satisfied our requirements.
3) According to the analysis result of modules distribution in the DTR system, redesign the architecture of hardware platform, which is based on TMS320DM6467 processor and corresponding Peripherals such as video switching chips, DDR2 SDRAM, NAND flash, etc. Designed platform highlights the features of DTR system.
引文
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[2]李颖,张占月,方秀花.空间目标监视系统发展现状及展望.国际太空. 2004年06期.
[3]李炎,康开平.美国空间监视系统最新发展及趋势分析[J].航天器工程. 2008, 17(2): 76-82.
[4]夏禹.美国将发射首颗“天基空间监视系统”卫星.国际太空. 2010年08期.
[5]魏晨曦.俄罗斯的空间目标监视、识别、探测与跟踪系统.中国航天. 2006年08期.
[6]赵霜,张社欣,方有培,汪立萍.美俄空间目标监视现状与发展研究[J].航天电子对抗. 2008, 24(1): 27-29.
[7]何立萍.国外空间侦察监视系统的发展[J].航天电子对抗. 2007, 23(6): 7-11.
[8]骆文辉,杨建军.国外空间监视系统的现状与发展.飞航导弹. 2008年04期.
[9]周立伟,刘玉岩.目标识别与探测.北京:北京理工大学出版社. 2002.
[10] Briassoul A, Mezarisv, Kompatsiarisl. Color aided motion segmentation and object tracking for video sequences semantic analysis[J]. International Journal of Imaging Systems and Technology. 2007, 17 (3): 174-189.
[11] Gevers T. Adaptive image segmentation by combining photometric invariant region and edge information[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 2002, 24 (6): 848-852.
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[14]赵瑶,常发亮.直方图统计方法的对比研究[J].计算技术与自动化. 2008, 26(4): 71-74.
[15] Wang De-min. Unsupervised video segmentation based on watersheds and temporal tracking[J]. IEEE Trans on Circuits & Systems for Video Technology.1998, 8 (5): 539-546.
[16] Meyer F, Beucher S. Morphological segmentation[J]. Journal of Visual Communication and Image Representation. 1990, 1(1) : 21-46.
[17] Choi J C, Lee S W, Kim S D. Spatio-temporal video segmentation using a joint similarity measure[J]. IEEE Trans on C ircuits & Systems for Video Technology. 1997, 7(2): 279-286.
[18] Castagno R, Ebrah Imi T, Kuntm. Video segmentation based on multiple features for interactive multimedia applications[J]. IEEE Trans on Circuits & Sys tems for Video Technology. 1998, 8(5): 562-571.
[19] Gonszalezr C, Woddsr E. Digital image processing[M]. 2nded. London: Prentice Hall, 2003.
[20] Haritaoglu L, Harwood D, Davisl S. W4: real-time surveillance of peop le and their activities[J]. IEEE Trans on Patte rn Analys is and Machine Intelligence. 2000, 22 (8): 809-830.
[21] Wren C, Azarbayejani A, Darrell T, et al. Real-time tracking of the human body[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 1997, 19(7): 780-785.
[22] Grimson Wei, Stauffer C, Romano R, et al. Using adaptive tracking to classify and monitor activities in a site[ C ] / /Proc of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1998: 22-29.
[23] Stauffer C, Grimson W. Learning pattern of activity using real-time tracking [J]. IEEE Trans on Patte rn Analys is and Machine Intelligence. 2000, 22(8): 747-757.
[24] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction[C] / /Proc of the 6th European Conference on Computer Vision. 2000: 751-767.
[25] Lipton A J, Fuj Iyoshih, Patil R S. Moving target classification and tracking from real-time video[C] / /Proc of the 4th IEEE Workshop on Applications of Computer Vision. 1998: 8-14.
[26]李刚,邱尚斌,林凌等.基于背景差法和帧间差法的运动目标检测方法[J].仪器仪表学报. 2006, 26 (8): 961-964.
[27]杨丽娟,刘教民,王震州等.基于分块帧差的视频图像运动检测[J].河北科技大学学报. 2006, 27 (1): 89-92.
[28] Baron J, Flee D, Beauchemin S. Performance of op tical flow techniques[J]. International Journal of Computer Vision. 1994, 12(1): 42-77.
[29]董琪.嵌入式系统现状诌议[J].一重技术. 2007, 05: 91-92.
[30] Kass M, Witkin A, Terzopolos D. Snakes: Active contour models[J]. International Journal of Computational Physics. 1987, 1(4): 321-331.
[31] Xu C, Pham D L, Prince J L. Image segmentation using deformable models[M]. SPIE handbooks on Medical imaging volume 3: Medical image analysis. California, May, 2000:129-174.
[32] Mumford D, Shah J. Optimal approximation by piecewise smooth functions and associated variational problems[J]. Communication on Pure and Applied Mathematics. 1989, 42(5): 577- 685.
[33] Chan T, Vese L A. A level set algorithm for minimizing the Mumford-Shah functional in image processing[C]. Proceedings of IEEE Workshop in Variational and Level Set Methods. Vancouver, Canada, 2001: 161-168.
[34] Schmidt F R, Cremers D. A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors[J]. DAGM Symposium on Pattern Recognition. 2009, 5748(2009): 31-40.
[35] Wang H, Zhang H. Adaptive Shape Prior in Graph Cut Segmentation[C]. IEEE International Conference on Image processing (ICIP). Hong Kong, 2010: 3029-3032.
[36] Chan T and Zhu W. Level set based shape prior segmentation[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). San Diego, 2005:1164-1170.
[37] Teboul O, Simon L et al. Segmentation of Building Facades Using Procedural Shape Priors[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Diego, 2010: 3105-3112.
[38]田昊,杨剑等.基于先验形状约束水平集模型的建筑物提取方法[J].自动化学报. 2010, 36(11) : 1502 - 1511.
[39]赵璐,于惠敏.基于先验形状信息和水平集方法的车辆检测[J].浙江大学学报(工学版). 2010, 44(1):125 - 130.
[40] Chan T F, Vese L A. Active contours without edges[J]. IEEE Transactions on Image Processing. 2001, 10(2): 266-277.
[41] Li C, Xu C, Gui C et al. Level Set Evolution without Re-initialization: A new variational Formulation[C]. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). San Diego, 2005:430-436.
[42]吴丰博.基于DSP的目标跟踪与识别系统[硕士论文].上海:上海交通大学. 2011.
[43]合众达. SEED-DVS6467基于TMS320DM6467的数字视频服务器解决方案硬件用户指南.北京合众达责任有限公司. 2009.
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