基于高速DSP的实时运动目标识别跟踪系统

研究了实时视频采集和高速目标识别检测、跟踪系统模型,分析了功能状态.在对检测、跟踪系统任务流和算法分析的基础上研究了基于高速DSP、大规模可编程器件和高速PCI通信总线架构的可行性系统实现方案.该系统采用复用总线设计,提高了系统资源利用率和系统可靠性.实验结果表明,该系统具有实时监测场景和在复杂背景下实时检测识别和跟踪运动目标的能力.     

基于Linux平台的智能监控和运动检测系统

采用ARM9和DSP双核CPU,以ARM9为控制器,通过TCP中的socket机制来建立网络通信,将摄像头采集的视频图像信息,传送给DSP处理器,处理后的数据在客户端进行解码显示.分析了一些热门的运动目标检测技术,并利用MATLAB进行了相应的实验验证,然后将核心算法代码移植到DaVinci嵌入式系统中.为实现运动目标检测算法在DaVinci嵌入式平台的运行,将嵌入式视觉库EMCV移植到DM6446平台,并在此基础上完成检测算法的集成工作,为了进行视频信息的远程显示,在系统软件的基础上还增加了视频编码及网络传输任务,实验结果表明,改进的VIBE算法在智能监控和运动目标检测具有良好的检测效果.     

基于DM6446的智能视频监控系统的设计

达芬奇技术是一款集成DSP处理器、软件、开发工具和技术支持的综合型解决方案系列。以达芬奇技术为支持,针对视频监控中的运动目标,引入自主跟踪思想,提出了一种基于双核TMS320DM6446的运动目标跟踪智能监控系统方案,完成了相应的软硬件设计。系统采用相邻帧差法进行运动检测,并通过运动目标形心偏移量控制云台实现运动目标跟踪。该方案作为单片系统,设计简单,具有良好的扩展性和灵活性,运行算法具有可重构性。     

运动目标检测与跟踪中FPGA的应用

运动目标检测与跟踪需要处理大量的视频数据,FPGA并行处理的工作方式和可重复执行多任务的能力,使其在视频数据运算与处理中能够发挥很大作用。讨论了FPGA结合DSP实现目标检测与跟踪的技术方案,重点研究了以FPGA构建图像预处理环节中处理器的设计方法。仿真和实验结果表明,基于FPGA的处理器能够完全满足图像预处理中运算量大、重复性强、速度高的要求,有效地缓解了DSP在后续的目标检测与跟踪中数据运算处理的压力,使得系统更加实时、准确、高效。     

基于DSP运动目标检测系统的设计与实现

随着DSP技术的发展,其在视频处理方面的应用得到广泛关注。本文采用TI公司的一款高性能的DSP设计出一种能够适应复杂环境的运动目标检测系统。该系统在C64x系列DSP的实时操作系统DSP/BIOS的环境下运行,实现了基于类/微驱动模型的视频采集驱动程序。系统采用差分算法进行运动目标检测,并且在RF5构架下成功对算法进行了DSP移植,现场试验取得了良好效果。     

电力系统视频监控中目标检测与跟踪算法研究

对变电站电力设备进行全天候实时监测是电力系统安全防范的有效手段。根据视频监控技术的原理和特点,介绍了视频监控系统中的两大关键技术---运动目标检测和跟踪技术,并对运动目标检测和跟踪技术中常用的算法进行了探讨。介绍了运动目标检测与跟踪算法的基本原理,分析了算法的特点以及存在的问题,选取了适用于电力系统的目标检测与跟踪算法。最后,设计了电力系统视频监控的基本模式,并对电力系统视频监控的实际运行系统及发展趋势进行了详细的分析。     

基于FPGA的实时视频运动背景补偿校正技术

从视频运动背景中检测与跟踪运动目标通常需要对背景进行补偿校正.本文提出了一种利用高性能FPGA实时实现的技术方案.该方法基于仿射参数模型求取视频图像的全局运动参数,利用块匹配的方法得到特征点的运动位移估计,利用匹配加权函数的方法剔除目标点和噪声点对全局运动参数估计的影响.在系统实现上采用多分辨率和并行流水线的处理结构,使系统资源占用和运算效率得到了较好的统一.     

基于ARM和DSP的滚动轴承检测系统中HPI接口的硬件设计

介绍了基于ARM和DSP的滚动轴承嵌入式故障诊断自动检测系统的框架,重点是检测系统中ARM与DSP通信的主机接口(HPI)的硬件电路实现和参数设置,并讨论了在与HPI通信时ARM出现的异常问题和解决办法.实践证明,本系统设计合理,运行稳定可靠,值得广泛推广应用.     

基于ARM与DSP的声频数据采集系统设计

结合1个基于ARM与DSP双核架构的声频数据采集系统的开发,介绍了ARM和DSP之间基于HPI通信的接口设计以及A/D、D/A接口设计,说明了接口工作原理及相关配置.提出了一种声频数据采集方案,利用ARM实现控制与人机交互,用DSP做信号处理,给出了数据采集的系统框图和软件流程图.该方案可充分利用DSP的McASP接口实现高精度A/D、D/A转换,利用HPI主机并行接口实现ARM与DSP之间的快速数据传输,从而实现海量声频数据流的高效、实时传输.     

基于Blackfin BF549的嵌入式动态目标实时跟踪系统设计

基于ADI公司Blackfin系列DSP BF549数字图像处理芯片,提出了一种在动态背景下对所采集图像中的动态目标进行实时跟踪与显示的嵌入式设计方案。对系统的工作原理、硬件及软件设计三方面进行了介绍。该设计方案充分利用BF549芯片的高性能视频处理能力,采用基于图像配准的运动目标检测方法,首先将采集的4幅相邻帧图像的背景进行配准,之后再利用差分相乘法,实现了动态背景下运动目标的检测,并利用球机对运动目标进行实时跟踪,最后将跟踪结果显示在TFT液晶屏幕上。     

Detecting intruders using time-series data by projection pattern of silhouette

This paper describes a method of detecting intruders by video image processing. There are some studies on detecting intruders in video image by analyzing difference-binary images and motion vectors. Because of the use of simple features such as size and motion vectors of changing regions, these methods are not fully able to reduce erroneous detection caused by other factors, such as small animals, swaying of trees, and changes in brightness. We propose a method of detecting intruders by time-series data on the projection pattern of the silhouette. In this method, changing regions in video images are extracted by the interframe differential technique and are transformed into projection patterns. Evaluation scores (a measure of similarity to a human silhouette) are calculated by flexible matching between observed patterns and the standard pattern. Discrimination processing is carried out in terms of a time-series score by tracking a target of each video frame. We have made experiments with a prototype and confirmed the effectiveness of this method. © 1997 Scripta Technica, Inc. Electr Eng Jpn 119(1): 62–73, 1997     

Pedestrian detection and tracking with night vision

This paper presents a method for pedestrian detection and tracking using a single night-vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road-detection module that provides key information for pedestrian validation. Experimental comparisons (e.g., grayscale SVM recognition versus binary SVM recognition and entire-body detection versus upper-body detection) have been carried out to illustrate the feasibility of our approach.     

Airborne video registration and traffic-flow parameter estimation

This paper investigates airborne helicopter video for estimating traffic parameters. Roll, pitch, and yaw of the helicopter make the video unstable, difficult to view, and the derived parameters less accurate. To correct this, a frame-by-frame video-registration technique using a feature tracker to automatically determine control-point correspondences is proposed. This converts the spatio-temporal video into temporal information, thereby correcting for airborne platform motion and attitude errors. The registration is robust, with the residual jitter being less than a few pixels over hundreds of frames. A simple vehicle-detection scheme identifies vehicle locations in the video, which are then tracked by the feature tracker, enabling us to estimate average velocity, instantaneous velocity, and other parameters automatically to within 10% of manual measurements. The entire process of registration, detection, tracking, and estimation takes only a few seconds for each frame. A prototype multimedia geographic information system (GIS) is created as a visualization tool for viewing the registered video, other airborne or satellite imagery, and data pertaining to georeferenced locations within a base map.     

Content-based event retrieval using semantic scene interpretationfor automated traffic surveillance

This paper proposes an object segmentation and tracking algorithm for visual surveillance applications. In order to detect moving objects from a dynamic background scene which may have temporal clutters such as swaying plants, we devised an adaptive background update method and a motion classification rule. A two-dimensional token-based tracking system using a Kalman filter is designed to track individual objects under occlusion conditions. We propose a new occlusion reasoning approach where we consider two different types of occlusion: explicit occlusion and implicit occlusion. By tracking individual objects with segmented data, we can generate motion trajectories and set a motion model using polynomial curve fitting. The trajectory model is used as an indexing key for accessing the individual object in the semantic level. We also propose an efficient way of indexing and searching based on object-specific features at different semantic levels. The proposed searching scheme supports various queries including query by example, query by sketch, and query on weighting parameters for event-based retrieval. When retrieving an interested video clip, the system returns the best matching event in the similarity order. In addition, we implement a temporal event graph for direct accessing and browsing of a specific event in the video sequence     

基于帧间差和肤色特征人脸检测与跟踪算法

在类似于电视会议这样的应用中，实现人脸的准确检测及跟踪是关键的任务。在文中，我们提出一种人脸检测和跟踪算法，本算法基于帧间差和肤色特征。实验结果表明，文中提出的方法能够在复杂背景下有效地实现人脸的检测和跟踪。     

A stable vision system for moving vehicles

This paper presents a novel approach to stabilize the output of video camera installed on a moving vehicle in a rugged environment. A 2.5D interframe motion model is proposed so that the stabilization system can perform in situations where significant depth changes are present and the camera has both rotation and translation. Inertial motion filtering is proposed in order to eliminate the vibration of the video sequences with enhanced perceptual properties. The implementation of this new approach integrates four modules: pyramid-based motion detection, motion identification and 2.5D motion parameter estimation, inertial motion filtering, and affine-based motion compensation. The stabilization system can smooth unwanted vibrations or shakes of video sequences and achieve real-time speed. We test the system on IBM PC compatible machines and the experimental results show that our algorithm outperforms many algorithms which require parallel pipeline image processing machines     

一种基于视频图像的运动车辆跟踪方法

基于视频图像的车辆运动跟踪是当前计算机视觉研究的热点,具有广泛的应用领域。本文研究了一种基于视频图像的运动车辆跟踪算法,通过当前帧目标边缘与实时更新模板的最优匹配来确定目标的位移量。首先启动了卡尔曼滤波,预测目标匹配搜索区域,然后再在搜索区域中利用边缘匹配精确定位目标,减小了匹配的计算量。实验结果表明,该方法是有效的且实时性好。     

煤矿井下人员视频图像识别跟踪的研究与应用

随着数字图像处理技术的发展,图像识别开始应用到煤矿的安全领域。基于背景差分法的研究,采用混合高斯模型的背景建模法,设计了井下视频图像中运动人员检测的流程和算法,来应对监测场景图像、光线强度的不断变化,并开发了实时监测平台,实现了对井下监控图像中人员进行识别和运动轨迹跟踪。实际应用结果验证,该算法能有效增强检测的效果,实现了在诸多不确定性因素的序列视频中较好的自适应构建背景,从而高效地检测出移动中的井下工作人员,并进行标注跟踪显示。