嵌入式下公共场所暴力行为视觉检测方法研究

根据暴力行为发生时往往存在肢体冲突,并伴随着身体全身或局部部位出现较剧烈运动的特点,提出了一种基于运动图像的公共场所暴力行为自动识别方法,并能够移植到嵌入式设备中以便于分布式智能监控.首先,采用Lucas-Kanade(LK)光流法获得相邻两帧监控图像间的光流场,通过光流分析法确定各个运动目标的运动状态,并提取它们的光流特征值、光流速度和方向;其次,对各个运动目标的光流特征值、光流速度与方向进行统计分析,以掌握各个运动目标的运动趋势;最后,结合光流场变化情况及运动目标聚集状态判断是否存在暴力行为.实验结果表明,该嵌入武系统识别准确性好、实时性强、可靠性高.     

基于拉格朗日场的多级运动特征暴力行为识别

针对暴力行为识别过程中缺乏描述不同时间尺度下暴力行为运动变化的问题,本文提出了一种基于拉格朗日场的多级运动特征暴力行为识别算法。该算法将描述非线性粒子运动的拉格朗日场引入暴力行为分析过程中,首先通过构建基于光流的拉格朗日场来挖掘不同时间尺度下暴力行为运动特征,设计了基于拉格朗日场的多级运动模块,该模块可以根据输入光流序列长度,计算多级运动特征;然后构建了基于流量门控制机制的双流网络,将多级运动特征和RGB图像特征融合;最后,利用LSTM和全连接模型计算识别结果。实验证明,该方法在公共暴力识别数据集上取得了很好的效果,特别是在真实监控场景的RWF-2000数据集上,暴力行为识别正确识别率可以达到88.4%,优于其他算法。      

基于LK光流法的微流控芯片中流体速度检测

为促进微流控技术相关领域的发展,针对目前微流控技术不能有效地检测流体在微通道的运动状态地问题,提出了一种基于LK光流法的检测技术,能有效地检测微流控芯片中流体的细微运动。实现过程为:通过在实时视频序列中捕获所需要的图像帧,选择ROI区域并对图像进行灰度化及高斯滤波平滑,然后划分网格使用LK光流法计算光流场,通过阈值分割筛选掉独立的光流矢量,最后将所剩的光流矢量转换为行列两个实值图像并求解出其像素距离,结合图像帧之间的时间差求出目标的运动速度。实验结果表明:像素速度标准差σ<1,具有可重复性;该方法能实时跟踪运动的液体,精度高、效率高,是一种非常可靠的检测方法。     

基于GPRS动态图像监控的设计与实现

提出了一种以嵌入式微处理器为核心、以嵌入式Linux系统为平台的动态图像监控系统。系统通过检测运动目标并保存运动目标图像,并通过GPRS发送给管理者。通过访问GPRS网络,对所监控的远程对象拍照并发送MMS消息,从而达到远程监控的目的。     

红外图像的光流计算

图像光流的计算不需要在图像序列中建立特征之间的对应关系，因此光流法在计算机视觉的众多领域，包括运动物体的参数估计和目标跟踪方面都有广泛的应用，由于红外图像的噪声相对较大，光流法很少用于红外图像中目标的运动参数估计和跟踪，这里，使用几种常用的光流计算方法对部分实际红外图像进行了光流场计算。结果表明，当选择合适的方法或计算方法进行一定的改进时，这些红外图像可以得到比较接近实际情况的目标光流场，进而应用于红外图像中的目标分割，运动状态分析与目标跟踪等领域。     

基于多重网格法的变分光流计算

光流是计算机视觉的重要组成部分,光流法是运动图像序列分析的重要方法,在许多应用中需要精确的估计,而基于变分的光流计算则能满足上述要求.提出一种新的变分模型,并将多重网格法运用于模型的计算,以最快速度求解线性方程组,近乎实时地得到图像序列的光流场.实验表明该方法是有效的.     

基于帧间差分和金字塔LK光流法的运动目标检测

针对用于运动目标检测的光流算法存在处理复杂、计算量大等问题,提出一种帧间差分算法和金字塔LK光流法相结合的运动目标检测方案.该方法先对视频图像进行帧间差分处理,得到图像的运动区域,再对该运动区域进行金字塔LK光流计算,减少了计算区域,提高目标检测的速度.最后在搭建的视觉避障平台上使用LabVIEW语言进行算法程序验证,实验结果证明了算法的有效性.     

动态背景下的光流场计算方法

通过计算光流场来检测场景中的运动目标是计算机视觉中非常重要的研究课题,而光流场计算的精度直接关系到目标检测的准确性。针对实际拍摄的视频中由于背景存在运动而导致光流场中运动目标不突出的情况,提出了一种基于分块积分投影配准算法的光流场计算方法。首先利用提出的分块积分投影配准算法得到图像背景的运动参数,然后对背景进行运动补偿,再利用L-K算法求取运动补偿后图像中有效区域的光流场。通过真实视频对算法进行验证,并将结果与经典的L-K算法结果进行了对比。对比结果显示:本文所提算法计算得到的光流场中运动目标更加突出,算法效果较好。     

基于立体视觉和光流的障碍物探测方法

车辆在行驶过程中必须能够检测和躲避障碍物,为减少交通事故的发生提出了一种用双目立体视觉和光流相结合方法。立体视觉通过基于相关性的边缘索引算法来获取可靠的视差值,通过基于梯度的光流算法分析连续图像序列获取光流场。为融合立体视觉和光流,分析得到运动目标的精确位置信息,运动方向和轨迹的第一步。     

基于图像光流的运动速度计算理论及应用

随着人们对运动处理兴趣的不断增长,运动目标的检测逐渐成为计算机视觉领域中最活跃的研究主题之一。作为一种新兴的序列图像运动分析方法,光流分析方法能够在不知道场景的情况下,检测出独立运动对象的速度。文章首先介绍光流运动分析的基本概念,然后介绍差分运动分析方法,接着引入基于图像光流的图像运动速度分析方法,最后,对光流法在未来序列图像速度计算中的应用前景提出展望。     

Motion detection using block based bi-directional optical flow method

Detecting moving objects from video frame sequences has a lot of useful applications in computer vision. This proposed method of moving object detection first estimates the bi-directional optical flow fields between (i) the current frame and the previous frame and between (ii) the current frame and the next frame. The bi-directional optical flow field is then subjected to normalization and enhancement. Each normalized and enhanced optical flow field is then divided into non-overlapping blocks. The moving objects are finally detected in the form of binary blobs by examining the histogram based thresholded values of such optical flow field of each block as well as the optical flow field of the candidate flow value. Our technique has been conceptualized, implemented and tested on real video data sets with complex background environment. The experimental results and quantitative evaluation establish that our technique achieves effective and efficient results than other existing methods.     

Prediction and tracking of moving objects in image sequences

We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropriately classifying the unlabeled and the occluding regions. Segmentation and optical flow tracking is used for predicting future frames.     

动态激光散斑用于透镜轴向象差的测量

在高斯光束照明下，根据在平面内匀速运动的散射体经光学系统在观察面内产生的动态散斑的复振幅和强度起伏的空－时互相关函数，给出了散斑纯沸腾状态的位置，这位置恰好是点光源经过透镜的成象位置，由此提出了一种测量透镜球差、象散和场曲的新方法，并与其它方法作了比较。     

Microwave imaging-Location and shape reconstruction frommultifrequency scattering data

The problem of determining the shape and location of an object embedded in a homogeneous dissipative medium from measurements of the field scattered by the object is considered in this paper. The object is assumed to be an infinite cylinder of known cross section illuminated by a TM plane wave and the scattered field is measured on a line segment perpendicular to the direction of incidence. Measurement data are carried out at three different frequencies for a homogeneous cylinder of known dielectric constant. The location and contour shape are determined using two different reconstruction algorithms, a Newton-Kantorovich (NK) method and the modified gradient (MG) method whose effectiveness and robustness are compared. Both methods are based on domain integral representations of the field in the body. They involve an iterative minimization of the defect between an integral representation of the field measured on the line and the actual measured data. The NK method involves a linearization of the nonlinear relation between the field and the contrast, as well as the solution of a direct scattering problem at each iteration. The MG method seeks the simultaneous reconstruction of the field and the characteristic function of the support of the scatterer without solving a direct problem at each step. Both methods employed the same initial guess and the a priori information that the characteristic function is nonnegative     

Traffic flow detection and statistics via improved optical flow and connected region analysis

Moving vehicle detection plays an important role in intelligent transportation systems. One of the common methods used in moving vehicle detection is optical flow. However, conventional Horn–Schunck optical flow consumes too much time when calculating dense optical flows so that it cannot meet the real-time requirements. This paper proposes a novel improved Horn–Schunck optical flow algorithm based on inter-frame differential method. In our algorithm, optical flow field distribution is only calculated for pixels with larger gray values in the difference image, while for other pixels we applied the iterative smooth. The number of vehicles in the videos of traffic conditions is counted by setting the virtual loop and detecting optical flow information. To extract the moving vehicle as accurately as possible, we also propose a method to obtain moving vehicle minimum bounding rectangle based on the connected region analysis. Finally, we compare the improved optical flow with other four optical flow algorithms in moving vehicle extraction and vehicle flow detection, from which our method gives a much more accurate result.     

Radiation From a Uniformly Moving Charge in an Anisotropic Plasma

The radiation from a point charge moving uniformly in a plasma is investigated when the charge is moving in the direction of an external magnetic field. In general there are two modes, for each of which all the components of the electric and magnetic field are present. The two parameters of interest in this problem are the ratio u/c/sub 0/ of the velocity of the charges to the free-space velocity of electromagnetic waves and the ratio R of the gyromagnetic frequency to the plasma frequency of the electrons. For two sets of values of these parameters the frequency and the angular spectrum of the emitted radiation are obtained. In certain cases, as many as three Cerenkov rays are found to propagate in the same direction; these multiple rays, however, correspond to different frequency components and to different modes of propagation. The motivation for this investigation is indicated briefly.     

基于光流和贝叶斯算法的目标跟踪研究

在本文中提出了一种方法来处理视频流中移动目标的跟踪,并且能够确定目标的移动速度。利用光流与贝叶斯方法在每一帧图像中检测对象,这种方法能够提高光流的检测性能,利用目标的质心像素位移来确定目标的移动距离,目标的速度是利用系列帧中目标移动的距离之间的帧数来计算的。实验表明,该方法能够有效地进行目标跟踪。     

Motion-based grouping of optical flow fields: the extrapolation andsubtraction technique

The goal of three-dimensional (3-D) motion segmentation is to identify the image areas projected by different moving objects in 3-D space. However, many prevailing methods merely detected the discontinuity of optical flow field and usually considered these boundaries as that produced by different 3-D motions. In fact, the flow discontinuity can be generated either by two different 3-D motions or by the structural discontinuity on the same moving object. The wrong identification causes several problems in 3-D motion estimation. A simple method called the extrapolation and subtraction (ES) technique is proposed to solve these problems. The input image flow field is first partitioned into several functionally analytic regions. Each analytic region is assumed to be projected by a roughly planar patch moving in 3-D space. Based on the parameterization of these analytic flow fields, the ES technique provides a very simple and fast method to test the 3-D motion compatibility between two interested analytic flow fields.