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用侧影特征分析和识别人的异常步态 总被引:1,自引:0,他引:1
基于计算机视觉的步态分析是计算机视觉领域的研究热点。目前的研究大多集中在通过对正常步态的分析实现身份识别,而通过异常步态分析来识别人的异常状况方面的研究却很少。提出了一种简单有效的基于计算机视觉的异常步态识别方法,通过人的宽高比提取反应步态特征的特征向量,然后用支持向量机进行异常步态的识别。实验结果表明了该方法的有效性。 相似文献
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精准养殖是一种实时监控奶牛信息并分析反馈的信息技术,其目的是保证奶牛福利.计算机视觉技术能够解决接触式方法带来的应激性与成本高问题.综述了奶牛精准养殖技术的发展现状,重点围绕人工智能技术和计算机视觉技术,针对个体身份识别、行为感知两个核心问题,从经典方法和深度学习方法两个方向,着重阐述对比应用对象、应用场景以及算法性能等.研究发现,个体身份识别和行为感知主要以图像分类算法为主;深度学习方法具有更好鲁棒性.最后总结了个体身份识别和行为感知方法的难点,展望了身份识别和行为感知未来发展方向. 相似文献
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车辆辅助驾驶系统中基于计算机视觉的行人检测研究综述 总被引:16,自引:0,他引:16
基于计算机视觉的行人检测由于其在车辆辅助驾驶系统中的重要应用价值成为当前计算机视觉和智能车辆领域最为活跃的研究课题之一. 其核心是利用安装在运动车辆上的摄像机检测行人,从而估计出潜在的危险以便采取策略保护行人.本文在对这一问题存在的困难进行分析的基础上,对相关文献进行综述. 基于视觉的行人检测系统一般包括两个模块:感兴趣区分割和目标识别,本文介绍了这两个模块所采用的一些典型方法,分析了每种方法的原理和优缺点. 最后对性能评估和未来的研究方向等一系列关键问题给予了介绍. 相似文献
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在基于视觉的物体识别领域,单目视觉识别技术往往只能获得物体片面信息,而多目视觉识别技术的运算复杂度较高.随着物联网技术的普及,无源超高频射频识别技术已经大规模应用于物体的标识上,具有的读取速度快、读取距离远的优势.本文提出一种通过先验信息辅助视觉识别的通用方法,提高物体识别的速度和准确性.通过识别射频识别标签,从数据库读取准确的特征信息辅助图像识别物体.通过摄像头采集物体的图像、视频信息后传输给控制模块,控制模块从数据库获取射频识别先验信息后以相机标定算法对图像进行矫正处理,对目标物体进行定位,从而复现物体的三维图像.传统的边缘检测和目标检测技术需要两个及以上的摄像头才能对物体进行三维识别,所提方法只需使用一个摄像头即可获取物体三维位置.针对边缘检测的改进中通过结合射频识别标签中准确的物体几何信息和像素信息来确定滤波窗口的权重,进行标定真实边缘和潜在边缘;针对目标检测的改进中在原始的Faster R-CNN的RPN架构上引入了特征金字塔,使得特征提取时语义更强.最后两种不同视觉识别方式的实验结果证明了所提方法的有效性,所提方法具有更高的识别定位精准度、更低的算法复杂度和更快的识别速率,可以更加准确可靠地对物体特性进行检测及形状、方位的判断. 相似文献
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基于计算机视觉的人群行为分析是一个非常活跃的研究领域,视频人数统计是人群行为分析的一个重要内容.本文对视频人数识别近年来的发展作了比较详细的论述,从基于特征点跟踪、基于区域的跟踪和基于模板匹配的跟踪三个方面分析近些年人数识别进展情况.文章通过对各种不同识别算法思想的研究,对当前该研究方向上亟待解决的问题做了比较详细的分析,并对未来人数识别的指出了一些改进方法. 相似文献
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随着信息社会对安全的要求不断提高,利用生物特征进行快速准确的身份识别成了当今的主流。与传统的身份鉴定手段相比,生物特征识别具有无可比拟的优势,特别是步态识别技术,由于其对系统分辨率要求低、远距离识别、非侵犯性和难以隐藏等特点而倍受计算机视觉研究者的关注。对步态识别所涉及的运动检测与跟踪、特征提取、特征处理以及模式识别分别进行了详细论述。 相似文献
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The humanID gait challenge problem: data sets, performance, and analysis 总被引:14,自引:0,他引:14
Sarkar S Phillips PJ Liu Z Vega IR Grother P Bowyer KW 《IEEE transactions on pattern analysis and machine intelligence》2005,27(2):162-177
Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology. 相似文献
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Heesung Lee Heejin Lee Euntai Kim 《International Journal of Control, Automation and Systems》2014,12(1):202-207
The recognition of a person from his or her gait has been a recent focus in computer vision because of its unique advantages such as being non-invasive and human friendly. However, gait recognition is not as reliable an identifier as other biometrics. In this paper, we applied a hierarchical fair competition-based parallel genetic algorithm and a neural network ensemble to the gait recognition problem. A diverse set of potential neural networks are generated to increase the reliability of the gait recognition, not only the best ones. Furthermore, a set of component neural networks is selected to build a gait recognition system such that generalization errors are minimized and negative correlation is maximized. Experiments are carried out with the NLPR and SOTON gait databases and the effectiveness of the proposed method for gait recognition is demonstrated and compared to previous methods. 相似文献
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Tracey K. M. Lee Mohammed Belkhatir Saeid Sanei 《Multimedia Tools and Applications》2014,72(3):2833-2869
Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available—for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance. 相似文献