光电工程, 2017, 44 (10): 972, 网络出版: 2017-11-27  

压缩域目标跟踪算法在小型化DSP平台上的研究与实现

Research and implementation of target tracking algorithm in compression domain on miniaturized DSP platform
作者单位
天津大学微光机电教育部重点实验室,天津 300072
摘要
本文对基于压缩感知的压缩域目标跟踪算法进行了研究,为满足特定的应用场合要求,针对原算法的不足进行了改进,同时基于小型化低成本目标位置探测器设计思想及需求,设计并实现了以TMS320DM6437数字信号处理器为核心的实时图像跟踪处理平台,对算法在该DSP平台进行了实现与优化。仿真和实验结果表明,经过结合卡尔曼滤波器、融合LBP特征以及添加自适应学习速率更新策略等措施,算法的鲁棒性得到提高;对算法在DSP中的实现,经过一系列优化措施,对分辨率为960×960的视频图像,当取目标窗口为80×80时,处理速度可达25 f/s,能够满足实时性跟踪要求。系统能够对选定的运动目标进行连续、稳定地跟踪,能够满足特定应用场合下的目标位置探测与跟踪需求,具有一定的实用性,同时也对该类目标跟踪方法在嵌入式平台的研究与应用具有一定的参考价值。
Abstract
The target tracking algorithm in compression domain based on compression perception is studied. To meet the specific application requirements, the shortcomings of original algorithm are improved. At the same time, based on the design idea and demand of miniaturized target position detector, a real-time image pro-cessing platform with TMS320DM6437 digital signal processor as the core is designed and implemented, and the algorithm is implemented and optimized on the DSP platform. The simulation and experiment results show that after the combination of Kalman filter, LBP feature and adding adaptive learning rate update strategy, the stability of the algorithm is improved. For the implementation in DSP, after a series of optimizing measures, as for an image with resolution of 960×960, taking the target window of 80×80 into account, the computation speed can be up to 25 fps, which can meet the requirement of real-time tracking. The embedded tracking system can track the selected moving objects continuously and stably, and can meet the target localization and tracking require-ments under specific applications, which has a real practical value. Moreover, the method in this paper has a certain reference value for the research and applications of this kind of target tracking method in the embedded platform.
参考文献

[1] 贾桂敏. 基于物体局部信息的跟踪算法研究[D]. 天津: 天津大学, 2008.

    Jia Guimin. Research of target tracking based on local infor-mation[D]. Tianjin: Tianjin University, 2008.

[2] 王睿, 王林, 姜志威. 基于DSP的主动视觉运动目标跟踪策略及实现[J]. 光电工程, 2009, 36(2): 6–10.

    Wang Rui, Wang Lin, Jiang Zhiwei. Active visual system for moving object intelligent tracking based on DSP[J]. Opto-Electronic Engineering, 2009, 36(2): 6–10.

[3] Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting[C]// Proceedings of British Machine Vision Confer-ence, 2006, 1: 6.

[4] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 2012, 34(7): 1409–1422.

[5] Zhang Kaihua, Zhang Lei, Yang M H. Real-time compressive tracking[C]//Proceedings of the 12th European Conference on Computer Vision, 2012.

[6] 李庆武, 朱国庆, 周妍, 等. 基于特征在线选择的目标压缩跟踪算法[J]. 自动化学报, 2015, 41(11): 1961–1970.

    Li Qingwu, Zhu Guoqing, Zhou Yan, et al. Object compressive tracking via online feature selection[J]. Acta Automatica Sinica, 2015, 41(11): 1961–1970.

[7] Liu Qingshan, Yang Jing, Zhang Kaihua, et al. Adaptive compressive tracking via online vector boosting feature selec-tion[J]. IEEE Transactions on Cybernetics, 2015, PP(99): 1–13, doi: 10.1109/TCYB.2016.2606512. (in Press)

[8] Chan Sixian, Zhou Xiaolong, Li Junwei, et al. Adaptive com-pressive tracking based on locality sensitive histograms[J]. Pattern Recognition, 2017, 72: 517–531.

[9] Yao Xuan, Zhou Yue. Real-time compressive tracking with a particle filter framework[C]//International Conference on Neural Information Processing, Cham, 2014: 242–249.

[10] Johnson W B, Lindenstrauss J. Extensions of Lipschitz map-pings into a Hilbert space[J]. Contemporary Mathematies, 1984, 26: 189–206

[11] Diaconis P, Freedman D. Asymptotics of graphical projection pursuit[J]. The Annals of Statistics, 1984, 12(3): 793–815.

[12] 潘秋萍, 杨万扣, 孙长银. 基于Haar与MB-LBP特征的车牌检测算法[J]. 东南大学学报(自然科学版), 2012, 42(Z1): 74–77.

    Pan Qiuping, Yang Wankou, Sun Changyin. License plate detection algorithm based on Haar and MB-LBP features[J]. Journal of Southeast University (Natural Science Edition), 2012, 42(Z1): 74–77.

[13] 房文涛, 王向军, 汤其剑. 基于粒子滤波的机载目标跟踪系统设计[J]. 激光与红外, 2012, 42(7): 841–844.

    Fang Wentao, Wang Xiangjun, Tang Qijian. Object tracking system for MUAV based on particle filter[J]. Laser & Infrared, 2012, 42(7): 841–844.

[14] 刘小宁, 陈晓冬, 郁道银. 基于DSP的运动目标识别与跟踪系统的设计[J]. 电视技术, 2010, 34(11): 107–110.

    Liu Xiaoning, Chen Xiaodong, Yu Daoyin. Design of moving target recognition and tracking system based on DSP[J]. Video Application & Project, 2010, 34(11): 107–110.

[15] 冯禹, 王向军, 陈文亮. 相对位姿测量解算的DSP实现[J]. 传感技术学报, 2016, 29(1): 35–39.

    Feng Yu, Wang Xiangjun, Chen Wenliang. DSP implementa-tion of relative position and attitude calculation[J]. Chinese Journal of Sensors and Actuators, 2016, 29(1): 35–39.

[16] 彭志明, 李琳. 基于IQmath库的定点DSP算法设计[J]. 单片机与嵌入式系统应用, 2010(9): 39–41.

    Peng Zhiming, Li Lin. Fixed-point DSP algorithm based on IQmath library[J]. Microcontrollers & Embedded System, 2010(9): 39–41.

[17] 朱威, 韩巨峰, 郑雅羽, 等. 基于DSP的全景视频多目标实时检测[J]. 光电工程, 2014, 41(5): 68–76.

    Zhu Wei, Han Jufeng, Zheng Yayu, et al. Real-time detection of multi-object based on DSP processor for panoramic video[J]. Opto-Electronic Engineering, 2014, 41(5): 68–76.

[18] 李文龙, 刘利, 汤志忠. 软件流水中的循环展开优化[J]. 北京航空航天大学学报, 2004, 30(11): 1111–1115.

    Li Wenlong, Liu Li, Tang Zhizhong. Loop unrolling optimization for software pipelining[J]. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(11): 1111–1115.

[19] 祝中科. 基于DM6467的车辆轮对磨耗检测算法的优化实现[D]. 杭州: 杭州电子科技大学, 2013.

    Zhu Zhongke. Optimized for vehicle wheelset wear detection algorithm based on DM6437[D]. Hangzhou: Hangzhou DianZi University, 2013.

程卫亮, 王向军, 万子敬, 郭志翼. 压缩域目标跟踪算法在小型化DSP平台上的研究与实现[J]. 光电工程, 2017, 44(10): 972. Weiliang Cheng, Xiangjun Wang, Zijing Wan, Zhiyi Guo. Research and implementation of target tracking algorithm in compression domain on miniaturized DSP platform[J]. Opto-Electronic Engineering, 2017, 44(10): 972.

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