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IMM目标跟踪算法在DSP上的优化与实现

Implementation and optimization of interacting multiple model for object tracking algorithm runing on DSP
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摘要 交互多模型(IMM)算法运算复杂、计算量大,很难满足高实时性系统要求,为此,提出了一种将IMM跟踪算法在TMS320C2000系列DSP芯片上实现的方法。根据DSP特点,利用C代码优化、汇编优化和指令流水线等技术对算法进行深度优化。实验结果表明,该方法具有运算效率高的特点,满足高实时性系统要求,采用的优化方法对其它算法的DSP优化有一定的借鉴意义。 The interacting multiple model algorithm is time-consuming and complex.It is not suitable for the high real-time system.In order to meet this requirement,an efficiently implementation of the IMM object-tracking algorithm using TMS320C2000 DSP is presented.Depending on the feature provided by the DSP architecture,this algorithm is highly optimized by C code optimization,assembly optimization and Instruction pipeline.The experiment proves that the method has advantage with high calculation efficiency.Also,it is suitable for the high real-time system.A series of the optimization method introduced also apply to the other DSP chip software optimization.
出处 《计算机工程与设计》 CSCD 北大核心 2014年第6期1915-1919,共5页 Computer Engineering and Design
基金 2013年辽宁省自然科学基金联合基金项目(2013024002) 辽宁省科学基金项目(20091059) 中国航空科学基金项目(2008ZC54) 沈阳市人才资源开发专项基金项目(SYRC201001)
关键词 交互多模型算法 目标跟踪 数字信号处理器 编译器 优化 IMM object-tracking DSP compiler optimize
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  • 1TI, TMS320C6000 optimizing C/C++ compiler user's guide[M]. Texas Instruments Incorporated, 2001.
  • 2TI, Cache usage in high-performance DSP applications with the TMS320C64x[M].Texas Instruments Incorporated, 2001.
  • 3TI, TMS320C64x EDMA performance data[M]. Texas Instruments Incorporated, 2004.
  • 41GB/T20090.2-2006,信息技术先进音视频编码.第2部分:视频[S].2006.
  • 5Tl,code composer studio user's guide[M].Texas Instruments Incorporated,2000.
  • 6LIU CHANGPING.Gabor-based kernel PCA with fractional power polynomial models for face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5):572-581.
  • 7VIOLA P,JONES M.Robust real-time object detection[C]// Second International Workshop on Statistical and Computational Theory of Vision-Modeling,Learning,Computing and Sample.Vancouver,Canada:[s.n.],2001:1-25.
  • 8VIOLA P,JONES M.Rapid object detection using a boosted cascade of simple features[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2001,1:511-518.
  • 9XU GAOFENG,HUANG LEI,LIU CHANGPING.Eye location using hierarchical classifier[C]// Proceedings of the 6th International Conference on Machine Learning and Cybernetics,Hong Kong:[s.n.],2007,4:2193-2197.
  • 10ZHANG H M,GAO W,CHEN X L.Learning informative features for spatial histogram-based object detection[C/OL]// Proceedings of International Joint Conference on Neural Networks.[2009-08-01].http://www.jdl.ac.cn/project/faceId/articles/RRJDL_HongmingZhang_IJCNN05.pdf.

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