基于DM6446平台的智能视频监控关键算法研究与实现
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摘要
智能视频监控以数字化、网络化技术为基础,代表着目前视频监控的发展方向。不同于以图像的采集、传输与存储为主的传统视频监控,智能视频监控在对图像采集编码的基础上,能够检测和识别不同的物体,发现监控现场中的异常情况,并及时地进行响应与报警。智能视频监控能够广泛地应用于安防、交通管理、商业服务等众多领域,具有良好的市场前景。
智能视频监控系统中使用到了很多计算机视觉和数字图像处理技术,本文就其中的一些关键性算法展开了讨论,主要针对目标检测、跟踪技术进行了深入的研究和实践,结合嵌入式平台的特点进行简单、有效的算法设计。本文提出了基于块的目标检测算法,在背景差法的基础上,采用以块为单位的处理进行进一步的判决。该方法在提高判决速度的同时,还能有效地消除小幅度噪声的影响。对于目标跟踪,则采用了Mean Shift与卡尔曼滤波相结合的方案,有效地利用了目标的颜色直方图信息与目标的运动状态信息,可以准确地实现跟踪。在此基础上,结合不同的应用场景提出了相应的异常事件检测算法,包括非法滞留、非法移位报警,非法入侵报警以及越线检测,具有一定的实用性和推广性。
TI公司的DaVinciTM技术是一组专门为数字视频设计的系统解决方案,而DM6446则是基于该技术的SoC处理器,它具有ARM+DSP的开放式双核架构,是一款功能强大的数字多媒体处理器,因此我们选择其作为算法实现的平台。我们首先在DSP端对各类算法进行封装并生成底层算法库。为了实现实时的视频处理,结合硬件平台的特点对算法进行了进一步的优化。同时针对ARM端的开发,设计实现了Linux应用程序。在此基础上,我们搭建了具备视频采集、处理、编码、传输与解码等整套功能的演示系统,对本文提出的目标检测和跟踪算法进行了验证和评估。实验结果表明,基于DM6446平台实现的算法具有较好的检测跟踪性能,能够实现对CIF分辨率视频的实时处理。
Intelligent video surveillance, which is based on digital and networked technologies, represents the orientation of the current video surveillance. Different from traditional video surveillance, intelligent video surveillance, which is based on video capture and coding, is able to detect and recognize specific objects, discover abnormal situations, then respond to it and alarm in time. Intelligent video surveillance is now extensively utilized in various areas like security, traffic control and business service, which enjoys a promising market prospect.
Many technologies of computer vision and digital image processing are utilized in intelligent video surveillance, and we focus on the essential technologies such as object detect and object track, combining with the embedded platform to design simple and effective algorithms. We’ve developed block based algorithm for object detect, making detection decisions based on the block unit processing with background subtraction. It will not only enhance the decision speed but also remove the effect of small noises. The combination of Mean Shift and Kalman Filter is adopted for object track, utilizing the information of both color histogram and motion state of the object, which help to guarantee the precision. Based on these we further design various security related algorithms such as illegal persistence alarm, illegal intrusion alarm and warning line applications, etc.
TI’s DaVinciTM technology is mainly designed for digital video system solutions. DM6446 is the SoC processor based on DaVinci, which is a powerful digital media processor that featured for its ARM+DSP structure. We choose it as the implementation platform. We first generate the basic layer of the algorithm libraries on the DSP-side. Further optimizations aiming at the hardware platform are performed to enhance the speed. Multi-thread applications are designed at ARM-side. At last we construct a demo system with a full range of functions to evaluate our algorithm. Experimental results show that the implemented algorithms achieve good performance in both detection and tracking. And real time processing is achieved for videos with CIF size.
智能视频监控系统中使用到了很多计算机视觉和数字图像处理技术,本文就其中的一些关键性算法展开了讨论,主要针对目标检测、跟踪技术进行了深入的研究和实践,结合嵌入式平台的特点进行简单、有效的算法设计。本文提出了基于块的目标检测算法,在背景差法的基础上,采用以块为单位的处理进行进一步的判决。该方法在提高判决速度的同时,还能有效地消除小幅度噪声的影响。对于目标跟踪,则采用了Mean Shift与卡尔曼滤波相结合的方案,有效地利用了目标的颜色直方图信息与目标的运动状态信息,可以准确地实现跟踪。在此基础上,结合不同的应用场景提出了相应的异常事件检测算法,包括非法滞留、非法移位报警,非法入侵报警以及越线检测,具有一定的实用性和推广性。
TI公司的DaVinciTM技术是一组专门为数字视频设计的系统解决方案,而DM6446则是基于该技术的SoC处理器,它具有ARM+DSP的开放式双核架构,是一款功能强大的数字多媒体处理器,因此我们选择其作为算法实现的平台。我们首先在DSP端对各类算法进行封装并生成底层算法库。为了实现实时的视频处理,结合硬件平台的特点对算法进行了进一步的优化。同时针对ARM端的开发,设计实现了Linux应用程序。在此基础上,我们搭建了具备视频采集、处理、编码、传输与解码等整套功能的演示系统,对本文提出的目标检测和跟踪算法进行了验证和评估。实验结果表明,基于DM6446平台实现的算法具有较好的检测跟踪性能,能够实现对CIF分辨率视频的实时处理。
Intelligent video surveillance, which is based on digital and networked technologies, represents the orientation of the current video surveillance. Different from traditional video surveillance, intelligent video surveillance, which is based on video capture and coding, is able to detect and recognize specific objects, discover abnormal situations, then respond to it and alarm in time. Intelligent video surveillance is now extensively utilized in various areas like security, traffic control and business service, which enjoys a promising market prospect.
Many technologies of computer vision and digital image processing are utilized in intelligent video surveillance, and we focus on the essential technologies such as object detect and object track, combining with the embedded platform to design simple and effective algorithms. We’ve developed block based algorithm for object detect, making detection decisions based on the block unit processing with background subtraction. It will not only enhance the decision speed but also remove the effect of small noises. The combination of Mean Shift and Kalman Filter is adopted for object track, utilizing the information of both color histogram and motion state of the object, which help to guarantee the precision. Based on these we further design various security related algorithms such as illegal persistence alarm, illegal intrusion alarm and warning line applications, etc.
TI’s DaVinciTM technology is mainly designed for digital video system solutions. DM6446 is the SoC processor based on DaVinci, which is a powerful digital media processor that featured for its ARM+DSP structure. We choose it as the implementation platform. We first generate the basic layer of the algorithm libraries on the DSP-side. Further optimizations aiming at the hardware platform are performed to enhance the speed. Multi-thread applications are designed at ARM-side. At last we construct a demo system with a full range of functions to evaluate our algorithm. Experimental results show that the implemented algorithms achieve good performance in both detection and tracking. And real time processing is achieved for videos with CIF size.
引文
[1]罗时鹏,浅析智能视频监控系统及其应用,网络与信息, 2007, vol. 21, no. 7, pp. 34-38
[2]田青,宛根训,田强,智能视频监控检测对比,中国安防, 2007, no. 3, pp. 65-68
[3]吕金刚,杨健全,文代明等,智能视频监控技术的应用与发展,通信电源技术, 2006, vol. 23, no. 5, pp. 62-67
[4]李子青,智能视频监控技术——自主创新引领未来,中国安防, 2007, no. 3, pp. 50-55
[5] Texas Instruments Incorporated, SPRS283, TMS320DM6446 Digital Media System On-Chip, 2005
[6] P. Shi, E. G. Jones, and Q. Zhu, Median model for background subtraction in intelligent transportation system, in Proceedings of SPIE, Imaging Processing: Algorithms and Systems III, 2004, vol. 5298, pp. 168-176
[7] S. Gupte, O. Masoud and R. F. K. Martin, et al, Detection and Classification of Vehicles, IEEE Transactions on Intelligent Transportation Systems, 2002, vol. 3, pp.37-47
[8] D. Farin, P. H. N. de With, and W. Effelsberg, Robust background estimation for complex video sequences, in Proceedings of IEEE International Conference on Image Processing (ICIP), 2003, vol. 1, pp. 145-148
[9] F. E. Alsaqre and B. Z. Yuan, Moving Object Segmentation for Video Surveillance and Conferencing Applications, in Proceedings of International Conference on Communication Technology, 2003, vol. 2, pp. 1856-1859
[10] S. Barotti, L. Lombardi, and P. Lombardi, Multi-module switching and fusion for robust video surveillance, in Proceedings of International Conference on Image Analysis and Processing(ICIAP), Sept. 2003, pp. 260-265
[11] S. S. Cheung and C. Kamath, Robust techniques for background subtraction in urban traffic video, in Proceedings of SPIE, Visual Communications and Image Processing(VCIP), 2004, vol. 5308, no. 2, pp. 881-892
[12] N. Ohta, A statistical approach to background subtraction for surveillance systems, in Proceedings of IEEE International Conference on Computer Vision(ICCV), Jul. 2001, vol. 2, pp. 481-486
[13] C. Stauffer and W. E. L. Grimson, Learning patterns of activity using real-time tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), Aug. 2000, vol. 22, pp. 747-757
[14] C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 1999, vol. 2, pp. 246-252
[15] Y. Sun and B. Yuan, Hierarchical GMM to handle sharp changes in moving object detection, Electronics Letters, 2004, vol. 40, pp. 801-802
[16] K-W. Lee and J. Kim, Moving object segmentation based on statistical motion model, Electronics Letters, 1999, vol. 35, pp. 1719-1720
[17] Q. Zang and R. Klette, Robust background subtraction and maintenance, in Proceedings of International Conference on Pattern Recognition(ICPR), 2004, vol. 2, pp. 90-93
[18] K-T-P. Pakorn and B. Richard, A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes, Image and Vision Computing, 2003, vol. 21, pp. 913-929
[19] N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, 1979, vol. 9, no. 1, pp. 62-69
[20]余松煜,周源华,张瑞,数字图像处理,上海,上海交通大学出版社, 2007
[21]张伟,基于视觉的运动车辆检测与跟踪,博士学位论文,上海,上海交通大学, 2007
[22] K. Fukunage and L. D. Hostetler, The Estimation of the Gradient of a Density Function with application in Pattern Recognition, IEEE Transactions on Information Theory, 1975, vol. 21, no. 1, pp. 32-40
[23] Y. Cheng, Mean shift, mode seeking and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, vol. 17, no. 8, pp. 790-799
[24] D. Comaniciu, V. Ramesh and P. Meer, Kernel-Based Object Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligences, 2003, vol. 25, no. 5, pp. 564-577
[25] D. Comaniciu, V. Ramesh and P. Meer, Real-Time Tracking of Non-Rigid Objects using Mean Shift, IEEE CVPR, 2000, pp. 1-8
[26] Changjiang Yang, R. Duraiswami and L. Davis, Similarity measure for nonparametric kernel density based object tracking, Eighteenth Annual Conference on Neural Information Processing Systems, Victoria, British Columbia, Canada: NIPS, 2004
[27] R. T. Collins, Mean-shift blob tracking through scale space, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 2, no. 5, pp. 234-240
[28]朱胜利,朱善安,李旭超,快速运动目标的Mean shift跟踪算法,光电工程, 2006, vol. 33, no. 5, pp. 66-70
[29] G. Welsh, G. Bishop, An Introduction To the Kalman Filter, Technical Report TR95-041, University of North Carolina at Chapel Hill, 1995
[30] Y. Bar-Shalom and T. Fortmann, Tracking and Data Association, Academic Press, 1988
[31] S. Julier and J. Uhlmann, A New Extension of the Kalman Filter to Nonlinear Systems, Proc. SPIE, 1997, vol. 3068, pp. 182-193
[32] L. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, 1989, vol. 77, no. 2, pp. 257-285
[33] M. S. Arulampalam, S. Maskell and N. Gordon, et al, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, 2002, vol. 50, no. 2, pp. 174-188
[34] Texas Instruments Incorporated, SPRV060, DaVinciTM Technology At-A-Glance, 2007
[35] Texas Instruments Incorporated, SPRY096, Reaping the Benefits of SoC Processors for Video Applications, 2007
[36] Wintech Digital Systems Technology Incorporated, DaVinci Evaluation Module Technical Reference, 2006
[37] Wintech Digital Systems Technology Incorporated, DVEVM Getting started Guide, 2006
[38] Texas Instruments Incorporated, SPRUE66C, DVEVM Getting Started Guide, 2007
[39] Texas Instruments Incorporated, SPRUEG8, DVSDK Getting Started Guide, 2006
[40] Texas Instruments Incorporated, SPRAAI6, Creating a TMS320DM6446 Audio Encode Example Using XDC Tools, 2007
[41] Texas Instruments Incorporated, SPRU352G, TMS320 DSP Algorithm Standard Rules and Guidelines User’s Guide, 2005
[42] Texas Instruments Incorporated, SPRU352G, TMS320 DSP Algorithm Standard Developer’s Guide, 2002
[43] Texas Instruments Incorporated, SPRUEC8B, xDAIS-DM(Digital Media) User Guide, 2007
[44] Texas Instruments Incorporated, SPRUED6A, Codec Engine Algorithm Creator User’s Guide, 2007
[45] Texas Instruments Incorporated, SPRUED5, Codec Engine Server Integrator’s Guide, 2007
[46] Texas Instruments Incorporated, SPRUE67B, Codec Engine Application Developer user’s Guide, 2007
[47] Joint Video Team of ISO/IEC JTC1/SC29/WG11 & ITU-T SG16 Q.6, JVT-G050, Draft ITU-T Recommendation H.264 and ISO/IEC 14496-10 AVC, Pattaya, 2003
[48] T. Wiegand, G. J. Sullivan, G. Bjontegaard and A. Luthra, Overview of the H.264/AVC Video Coding Standard, IEEE Transactions on Circuits and Systems for Video Technology, 2003, vol. 13, no. 7, pp. 560-576
[49] I. E. G. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia, John Wiley & Sons Ltd, 2003, pp. 159-223
[50]宋磊, H.264视频编码算法在Ti DM642平台上的实现与优化,硕士学位论文,上海,上海交通大学, 2007
[51] Texas Instruments Incorporated, SPRU360, TMS320 DSP Algorithm Standard API Reference, 2002
[52] Texas Instruments Incorporated, SPRU187K, Optimizing Compiler User’s Guide, 2002
[53] Texas Instruments Incorporated, SPRU732D, TMS320C64x/C64x+ DSP CPU and Instruction Set Reference Guide, 2007
[54] Texas Instruments Incorporated, SPRUEV5, xDAIS DSKT2 User’s Guide, 2007
[55] Texas Instruments Incorporated, SPRAAG1, Using DMA with Framework Components for‘C64x+, 2007
[56] Texas Instruments Incorporated, SPRU007G, DSP/BIOS 5.20 Textual Configuration User’s Guide, 2005
[57] Texas Instruments Incorporated, SPRU403L, TMS320C6000 DSP/BIOS 5.30 Application Programming Interface Reference Guide, 2006
[58] Texas Instruments Incorporated, SPRU862A, TMS320C64x+ DSP Cache User's Guide, 2006
[2]田青,宛根训,田强,智能视频监控检测对比,中国安防, 2007, no. 3, pp. 65-68
[3]吕金刚,杨健全,文代明等,智能视频监控技术的应用与发展,通信电源技术, 2006, vol. 23, no. 5, pp. 62-67
[4]李子青,智能视频监控技术——自主创新引领未来,中国安防, 2007, no. 3, pp. 50-55
[5] Texas Instruments Incorporated, SPRS283, TMS320DM6446 Digital Media System On-Chip, 2005
[6] P. Shi, E. G. Jones, and Q. Zhu, Median model for background subtraction in intelligent transportation system, in Proceedings of SPIE, Imaging Processing: Algorithms and Systems III, 2004, vol. 5298, pp. 168-176
[7] S. Gupte, O. Masoud and R. F. K. Martin, et al, Detection and Classification of Vehicles, IEEE Transactions on Intelligent Transportation Systems, 2002, vol. 3, pp.37-47
[8] D. Farin, P. H. N. de With, and W. Effelsberg, Robust background estimation for complex video sequences, in Proceedings of IEEE International Conference on Image Processing (ICIP), 2003, vol. 1, pp. 145-148
[9] F. E. Alsaqre and B. Z. Yuan, Moving Object Segmentation for Video Surveillance and Conferencing Applications, in Proceedings of International Conference on Communication Technology, 2003, vol. 2, pp. 1856-1859
[10] S. Barotti, L. Lombardi, and P. Lombardi, Multi-module switching and fusion for robust video surveillance, in Proceedings of International Conference on Image Analysis and Processing(ICIAP), Sept. 2003, pp. 260-265
[11] S. S. Cheung and C. Kamath, Robust techniques for background subtraction in urban traffic video, in Proceedings of SPIE, Visual Communications and Image Processing(VCIP), 2004, vol. 5308, no. 2, pp. 881-892
[12] N. Ohta, A statistical approach to background subtraction for surveillance systems, in Proceedings of IEEE International Conference on Computer Vision(ICCV), Jul. 2001, vol. 2, pp. 481-486
[13] C. Stauffer and W. E. L. Grimson, Learning patterns of activity using real-time tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), Aug. 2000, vol. 22, pp. 747-757
[14] C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 1999, vol. 2, pp. 246-252
[15] Y. Sun and B. Yuan, Hierarchical GMM to handle sharp changes in moving object detection, Electronics Letters, 2004, vol. 40, pp. 801-802
[16] K-W. Lee and J. Kim, Moving object segmentation based on statistical motion model, Electronics Letters, 1999, vol. 35, pp. 1719-1720
[17] Q. Zang and R. Klette, Robust background subtraction and maintenance, in Proceedings of International Conference on Pattern Recognition(ICPR), 2004, vol. 2, pp. 90-93
[18] K-T-P. Pakorn and B. Richard, A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes, Image and Vision Computing, 2003, vol. 21, pp. 913-929
[19] N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, 1979, vol. 9, no. 1, pp. 62-69
[20]余松煜,周源华,张瑞,数字图像处理,上海,上海交通大学出版社, 2007
[21]张伟,基于视觉的运动车辆检测与跟踪,博士学位论文,上海,上海交通大学, 2007
[22] K. Fukunage and L. D. Hostetler, The Estimation of the Gradient of a Density Function with application in Pattern Recognition, IEEE Transactions on Information Theory, 1975, vol. 21, no. 1, pp. 32-40
[23] Y. Cheng, Mean shift, mode seeking and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, vol. 17, no. 8, pp. 790-799
[24] D. Comaniciu, V. Ramesh and P. Meer, Kernel-Based Object Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligences, 2003, vol. 25, no. 5, pp. 564-577
[25] D. Comaniciu, V. Ramesh and P. Meer, Real-Time Tracking of Non-Rigid Objects using Mean Shift, IEEE CVPR, 2000, pp. 1-8
[26] Changjiang Yang, R. Duraiswami and L. Davis, Similarity measure for nonparametric kernel density based object tracking, Eighteenth Annual Conference on Neural Information Processing Systems, Victoria, British Columbia, Canada: NIPS, 2004
[27] R. T. Collins, Mean-shift blob tracking through scale space, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 2, no. 5, pp. 234-240
[28]朱胜利,朱善安,李旭超,快速运动目标的Mean shift跟踪算法,光电工程, 2006, vol. 33, no. 5, pp. 66-70
[29] G. Welsh, G. Bishop, An Introduction To the Kalman Filter, Technical Report TR95-041, University of North Carolina at Chapel Hill, 1995
[30] Y. Bar-Shalom and T. Fortmann, Tracking and Data Association, Academic Press, 1988
[31] S. Julier and J. Uhlmann, A New Extension of the Kalman Filter to Nonlinear Systems, Proc. SPIE, 1997, vol. 3068, pp. 182-193
[32] L. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, 1989, vol. 77, no. 2, pp. 257-285
[33] M. S. Arulampalam, S. Maskell and N. Gordon, et al, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, 2002, vol. 50, no. 2, pp. 174-188
[34] Texas Instruments Incorporated, SPRV060, DaVinciTM Technology At-A-Glance, 2007
[35] Texas Instruments Incorporated, SPRY096, Reaping the Benefits of SoC Processors for Video Applications, 2007
[36] Wintech Digital Systems Technology Incorporated, DaVinci Evaluation Module Technical Reference, 2006
[37] Wintech Digital Systems Technology Incorporated, DVEVM Getting started Guide, 2006
[38] Texas Instruments Incorporated, SPRUE66C, DVEVM Getting Started Guide, 2007
[39] Texas Instruments Incorporated, SPRUEG8, DVSDK Getting Started Guide, 2006
[40] Texas Instruments Incorporated, SPRAAI6, Creating a TMS320DM6446 Audio Encode Example Using XDC Tools, 2007
[41] Texas Instruments Incorporated, SPRU352G, TMS320 DSP Algorithm Standard Rules and Guidelines User’s Guide, 2005
[42] Texas Instruments Incorporated, SPRU352G, TMS320 DSP Algorithm Standard Developer’s Guide, 2002
[43] Texas Instruments Incorporated, SPRUEC8B, xDAIS-DM(Digital Media) User Guide, 2007
[44] Texas Instruments Incorporated, SPRUED6A, Codec Engine Algorithm Creator User’s Guide, 2007
[45] Texas Instruments Incorporated, SPRUED5, Codec Engine Server Integrator’s Guide, 2007
[46] Texas Instruments Incorporated, SPRUE67B, Codec Engine Application Developer user’s Guide, 2007
[47] Joint Video Team of ISO/IEC JTC1/SC29/WG11 & ITU-T SG16 Q.6, JVT-G050, Draft ITU-T Recommendation H.264 and ISO/IEC 14496-10 AVC, Pattaya, 2003
[48] T. Wiegand, G. J. Sullivan, G. Bjontegaard and A. Luthra, Overview of the H.264/AVC Video Coding Standard, IEEE Transactions on Circuits and Systems for Video Technology, 2003, vol. 13, no. 7, pp. 560-576
[49] I. E. G. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia, John Wiley & Sons Ltd, 2003, pp. 159-223
[50]宋磊, H.264视频编码算法在Ti DM642平台上的实现与优化,硕士学位论文,上海,上海交通大学, 2007
[51] Texas Instruments Incorporated, SPRU360, TMS320 DSP Algorithm Standard API Reference, 2002
[52] Texas Instruments Incorporated, SPRU187K, Optimizing Compiler User’s Guide, 2002
[53] Texas Instruments Incorporated, SPRU732D, TMS320C64x/C64x+ DSP CPU and Instruction Set Reference Guide, 2007
[54] Texas Instruments Incorporated, SPRUEV5, xDAIS DSKT2 User’s Guide, 2007
[55] Texas Instruments Incorporated, SPRAAG1, Using DMA with Framework Components for‘C64x+, 2007
[56] Texas Instruments Incorporated, SPRU007G, DSP/BIOS 5.20 Textual Configuration User’s Guide, 2005
[57] Texas Instruments Incorporated, SPRU403L, TMS320C6000 DSP/BIOS 5.30 Application Programming Interface Reference Guide, 2006
[58] Texas Instruments Incorporated, SPRU862A, TMS320C64x+ DSP Cache User's Guide, 2006