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   运动人体识别 的翻译结果: 查询用时:0.011秒
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运动人体识别
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  “运动人体识别”译为未确定词的双语例句
     Optimization for the algorithm of human body recognition based on DSPs
     基于DSP的运动人体识别算法实现及其优化
短句来源
     Research on Moving Human Body Recognition System based on DSP
     基于DSP的运动人体识别系统研究
短句来源
     The human body recognition system based on video processing platform has obtained more and more attentions nowadays, for the video processing system can achieve the goal of it more effectively, and the technology of digital signal processors develops rapidly.
     运动人体识别是机器视觉研究中一个非常重要的研究领域,由于视频处理系统能够非常有效地实现这个目标以及数字信号处理器(DSP)的快速发展,基于视频处理平台的人体监控系统正越来越多地被人们关注。
短句来源
     The algorithm of human body recognition includes four parts of human body extracting, head locating, limb segmentation and body modeling.
     运动人体识别算法由人体目标提取、头部定位、肢体分割和人体建模四部分组成。
短句来源
  相似匹配句对
     Human Motion Recognition and Simulation Based on Retrieval
     基于检索的人体运动识别和模拟
短句来源
     Research on Moving Human Body Recognition System based on DSP
     基于DSP的运动人体识别系统研究
短句来源
     Field-tracking and Auto-rccogritaing in Human Body Moving
     人体运动的区域跟踪及部位自动识别
短句来源
     Algorithm for Recognizing the Markers in Human Motion Detection
     人体运动检测中的标志点识别算法
短句来源
     Research in Recognizing Device of Human Motion Based on Tracing Robot
     跟踪机器人识别运动人体装置的实现方案
短句来源
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The video surveillance systems are demanding to monitor moving human behaviors, specifically for such environments as jails, warehouses, secret rooms, offices and auditoriums. Accordingly, it is an imperative to differentiate moving human bodies from other moving objects rather than make a difference amongst human bodies. In this study, the frame difference method is employed for image segmentations of moving targets. By matching such human features as appearances, colors and postures, an algorithm for moving...

The video surveillance systems are demanding to monitor moving human behaviors, specifically for such environments as jails, warehouses, secret rooms, offices and auditoriums. Accordingly, it is an imperative to differentiate moving human bodies from other moving objects rather than make a difference amongst human bodies. In this study, the frame difference method is employed for image segmentations of moving targets. By matching such human features as appearances, colors and postures, an algorithm for moving human body recognition (AMHBR) is established to detect moving targets efficiently. In addition, this algorithm can be adapted for resource-restricted cases e.g. embedded systems. Based on experimental results, it is found that images of moving human bodies have been robustly and accurately segmented from different backgrounds. Therefore, this algorithm can be pragmatically applied for video surveillance systems via automatically controlling lenses towards moving human bodies.

许多监视系统要求能对运动人体的行为进行监视,如监狱、仓库、保密室、办公室、会场等场景,对运动人体的识别特点是区分运动人体与其他运动目标,而非判别人的个体间差别.以帧间差分法分割运动目标图像为基础,通过人的外形模式匹配、肤色匹配和姿态匹配的融合构造了一种识别运动目标中人体运动目标的算法(AMHBR),该算法的特点是简洁高效、适合嵌入式系统等资源受限的监视系统.通过试验证明,该算法能在不同的场景中分割出运动人体图像,具有很好的鲁棒性和准确度,达到了实用的程度.该算法对于监视系统中自动控制镜头对准运动人体具有实际意义.

The algorithm of human body recognition includes four parts of human body extracting, head locating, limb segmentation and body modeling. The principle of the algorithm is proposed, and the main functions and reasons to restrain algorithm speed are analyzed. The runtime of the algorithm to segment human body and limbs takes up 96% of the whole algorithm. It is found that Fourier transform and moving vector calculation are major factors to limit the speed of limb segmentation. It is also shown that mean filter...

The algorithm of human body recognition includes four parts of human body extracting, head locating, limb segmentation and body modeling. The principle of the algorithm is proposed, and the main functions and reasons to restrain algorithm speed are analyzed. The runtime of the algorithm to segment human body and limbs takes up 96% of the whole algorithm. It is found that Fourier transform and moving vector calculation are major factors to limit the speed of limb segmentation. It is also shown that mean filter and morphologic filter are bottleneck of the algorithm of human body extracting. According to hardware structure of TMS320C6000 DSPs, the optimum strategies for algorithm are put forward, which include compiler optimal parameters selecting, intrinsic function using, loops disassembling, memory dependencies eliminating, and standard library function calling. Experimental results show that the algorithm speed is well improved.

运动人体识别算法由人体目标提取、头部定位、肢体分割和人体建模四部分组成。文中阐述了算法的基本原理,分析了影响算法速度的主要函数和原因。分析发现人体目标提取和肢体分割模块占用了算法时间的96%。傅里叶变换和移动向量计算成为影响肢体分割算法速度的“瓶颈”,而中值滤波和形态学滤波则是影响人体目标提取算法速度的“瓶颈”因素。针对TMS320C6000DSP体系结构特点,提出了算法优化策略。通过合理配置编译器优化选项、采用内联函数、分解多层循环、指定存储器相关性,以及合理选用标准库函数的方法对算法进行了优化。实验结果表明,优化后的算法执行速度大大提高。

 
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