Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

A Skeletal Camera Network for Close-range Images with a Data Driven Approach in Analyzing Stereo Configuration

Structure-from-Motion(SfM)techniques have been widely used for 3D geometry reconstruction from multi-view images.Nevertheless,the efficiency and quality of the reconstructed geometry depends on multiple factors,i.e.,the base-height ratio,intersection angle,overlap,and ground control points,etc.,which are rarely quantified in real-world applications.To answer this question,in this paper,we take a data-driven approach by analyzing hundreds of terrestrial stereo image configurations through a typical SfM algorithm.Two main meta-parameters with respect to base-height ratio and intersection angle are analyzed.Following the results,we propose a Skeletal Camera Network(SCN)and embed it into the SfM to lead to a novel SfM scheme called SCN-SfM,which limits tie-point matching to the remaining connected image pairs in SCN.The proposed method was applied in three terrestrial datasets.Experimental results have demonstrated the effectiveness of the proposed SCN-SfM to achieve 3D geometry with higher accuracy and fast time efficiency compared to the typical SfM method,whereas the completeness of the geometry is comparable.

Web-Based Object Tracking Using Collaborated Camera Network

The paper presents a web based vision system using a networked IP camera for tracking objects of interest. Three critical issues are addressed in this paper. First is the detection of moving objects in the foreground;second is the control of pan-tilt-zoom (PTZ) IP cameras based on object location;and third is the collaboration of multiple cameras over the network to track objects of interests independently. The developed system utilized a network of PTZ cameras along with a number of software tools for this implementation. The system was able to track a single and multiple objects successfully. The difficulties in the detection of moving objects are also analyzed while multiple cameras are collaborating over a network utilizing PTZ cameras.

Automatic Service Discovery of IP Cameras over Wide Area Networks with NAT Traversal

A novel framework for remote service discovery and access of IP cameras with Network address Translation (NAT) traversal is presented in this paper. The proposed protocol, termed STDP (Service Trader Discovery Protocol), is a hybrid combination of Zeroconf and SIP (Session Initial Protocol). The Zeroconf is adopted for the discovery and/or publication of local services;whereas, the SIP is used for the delivery of local services to the remote nodes. In addition, both the SIP-ALG (Application Layer Gateway) and UPnP (Universal Plug and Play)-IGD (Internet Gateway Device) protocols are used for NAT traversal. The proposed framework is well-suited for high mobility applications where the fast deployment and low administration efforts of IP cameras are desired.

Modeling Camera Image Formation Using a Feedforward Neural Network

One fundamental problem in computer vision and image processing is modeling the image formation of a camera, i.e., mapping a point in three-dimensional space to its projected position on the camera’s image plane. If the relationship between the space and the image plane is assumed to be linear, the relationship can be expressed in terms of a transfor-mation matrix and the matrix is often identified by regression. In this paper, we show that the space-to-image relation-ship in a camera can be modeled by a simple neural network. Unlike most other cases employing neural networks, the structure of the network is optimized so as for each link between neurons to have a physical meaning. This makes it possible to effectively initialize link weights and quickly train the network.

基于多摄像头边云协同视频请求的智能监控预警方法研究

多摄像头边云协同视频已被广泛运用于智能化生活的各个领域,采用边云协同模式已然成为摄像头视频智能监控的数据处理的发展趋势。为了实现对多摄像头边云协同的视频请求任务的高精度检测,此次研究提出在基于多摄像头边云协同请求下的智能监控预警方法。采用边缘计算和云计算的紧密协同处理,在边云协同的数据协同能力下以较低成本完成对数据的高效处理。在多摄像头请求下的边云协同视频的智能监控预警,对视频数据进行处理与分析,提出利用网状关联分析的智能预警模型进行判定。对单摄像头与多摄像头下智能监控预警方法检测的准确率进行分析。得出该方法的检测准确率最高,且每项的检测准确率均在97%左右,证明了其在智能监控预警中的可靠性与有效性。

火山SO_(2)排放速率反演

SO_(2)紫外相机因在时间分辨率、空间分辨率、探测灵敏度以及探测精度等诸多方面均具有显著优势而成功应用于火山活动监测及其动力学研究。为解决紫外相机反演SO_(2)排放速率容易受烟羽湍流及图像低对比度影响等问题,提出了融入神经网络的光流算法。首先,基于大气紫外辐射传输特性,阐述了SO_(2)紫外相机的工作机理及SO_(2)浓度图像的反演方法;其次,将神经网络融入光流算法,实现了火山烟羽图像中SO_(2)排放速率的精确反演;最后,与传统光流法进行对比,论证了神经网络光流算法的科学性及优越性与精确性。实验结果表明:在图像低对比度及烟羽湍流效应的双重影响下,神经网络光流法可以把边缘反演的误差从94%降低至5%,显著提高了SO_(2)排放速率反演的精确性。

基于事件信息与深度学习的高动态范围三维重建

采用光学三维成像技术测量金属零件、黑色物体以及半透明物体等高动态范围(High dynamic range,HDR)表面的三维轮廓是一个极具挑战性的问题。目前,传统方法对存在较低反射以及半透明区域的场景进行重建还有一定的局限性,半透明物体的内部反射噪声很难消除。现有基于深度学习的方法通常使用相对较强的激光强度,这可能会损坏样品,同时会出现采集图像过曝现象,需要对激光强度进行繁琐的调整。针对这些问题,本文提出基于事件信息和深度学习算法的高动态场景三维测量方法。事件相机通过异步记录单个像素的亮度变化,无需等待全局曝光时间,具有高动态响应范围,能够充分采集到HDR场景的激光条纹反射信息。引入深度卷积神经网络(Deep convolutional neural network,DCNN)来消除半透明物体的内部噪声以及金属物体高反光的过曝影响,同时增强弱激光条纹图像质量。实验结果表明,本文方法能够应用低功率线激光扫描成功实现HDR场景的高质量三维重建。

基于Smart Camera的目标识别和加密传输

提出用一种以DSP为硬件核心的智能数字网络摄像机Smart Camera取代传统摄像机。它能够在对监控图像进行目标识别及其信息提取的同时,将特征信息经过RSA加密后通过网络传输,并可根据后方PC机的远程控制,给出不同的报警需求,实现了摄像机的智能化、数字化、网络化以及多用途。

面向高速移动场景的高速公路视频采集和自动化传输系统设计

针对高速移动带来的多普勒频移以及子载波干扰,设计了面向高速移动场景下的高速公路视频采集和自动化传输系统。通过数据采集层获取高速公路开关量信号以及视频信息,由数据处理层对其作降噪及增强,采用数据传输层的无线数字传输网络调用基于BEM-wiener的信道估计算法确定高速公路视频传输信道,完成视频信息的上传后,通过应用层分析,实现高速公路路况及突发异常事件的监测。通过试验,得出该系统可采集高速公路视频图像,视频图像清晰、视野宽阔、路况信息丰富;高速移动场景下高速公路视频数据包接收成功率达到0.9左右,BER指标值最小,数据传输效率高,传输时间最多不超过2.5 s,且丢包率小,最大丢包率为1.25%,具有较强的应用性。

卷积神经网络的神华关键配件状态自动跟踪研究

为避免关键配件异常状态带来的神华铁路货车运行安全隐患,提出基于卷积神经网络的神华关键配件状态自动跟踪方法。神华关键配件状态图像作为卷积神经网络的输入数据,经过卷积和池化操作后获得神华关键配件状态检测结果,并将其作为图像第一帧的状态,然后利用核相关滤波训练获得的回归模型估计图像下一帧的状态,实现神华关键配件状态自动跟踪。实验结果表明:该方法能够获得较为完整、清晰的神华关键配件状态图像;不同神华关键配件状态检测的MCC值均在0.8以上,且能够在异常状态发生之前得到状态检测结果;各时刻的神华关键配件状态跟踪结果与实际结果完全相同。

Energy Efficient Content Based Image Retrieval in Sensor Networks

The presence of increased memory and computational power in imaging sensor networks attracts researchers to exploit image processing algorithms on distributed memory and computational power. In this paper, a typical perimeter is investigated with a number of sensors placed to form an image sensor network for the purpose of content based distributed image search. Image search algorithm is used to enable distributed content based image search within each sensor node. The energy model is presented to calculate energy efficiency for various cases of image search and transmission. The simulations are carried out based on consideration of continuous monitoring or event driven activity on the perimeter. The simulation setups consider distributed image processing on sensor nodes and results show that energy saving is significant if search algorithms are embedded in image sensor nodes and image processing is distributed across sensor nodes. The tradeoff between sensor life time, distributed image search and network deployed cost is also investigated.

基于物联网的配网一二次融合跨区协同全景监测技术

针对配网一二次融合跨区协同全景监测技术过度依赖网络环境,当网络发生堵塞时会造成监测存在死角的问题,提出基于物联网的配网一二次融合跨区协同全景监测技术。设计监测智能物理架构,支持多网多工下监测节点之间的横向交互和纵向交互,在此基础上,计算监控摄像头的旋转角度,采集监测环境及监测目标图像,通过全景图像拼接配准,实现对目标的全景监测。实验结果表明:设计的基于物联网的全景监测技术在通信组网内,全景监测角度精准,没有出现监测死角,实用性较好。

基于BP神经网络的密封胶注胶轨迹在线调整方法

提出了一种基于BP神经网络的密封胶注胶轨迹在线自适应调整的方法。前期采集大量数据给BP神经网络模型学习,然后将BP神经网络模型与3D相机的实时探测结果相结合,计算得到当前产品注胶轨迹偏差的调整值。接着,将该偏差调整值通过网线传递到三轴耦合伺服控制系统,最后三轴耦合伺服控制系统实时更新密封胶注胶轨迹的NC程序,从而实现密封胶注胶轨迹的在线自适应调整。实验结果表明,该方法能够有效地提高密封胶轨迹的位置精度,降低产品报废率,并且减少了对个人能力和经验的依赖,减少了人工调整密封胶注胶轨迹所需要的时间,降低了调试员的工作强度,提高了生产效率。

An Automatic System of Vehicle Number-Plate Recognition Based on Neural Networks

This paper presents an automatic system of vehicle number-plate recognition based on neural networks. In this system, location of number-plate and recognition of characters in number-plate can be automatically completed. Pixel colors of Number-plate area are classified using neural network, then color features are extracted by analyzing scanning lines of the cross-section of number-plate. It takes full use of number-plate color features to locate number-plate. Characters in number-plate can be effectively recognized using the neural networks. Experimental results show that the correct rate of number-plate location is close to 100%, and the time of number-plate location is less than 1 second. Moreover, recognition rate of characters is improved due to the known number-plate type. It is also observed that this system is not sensitive to variations of weather, illumination and vehicle speed. In addition, and also the size of number-plate need not to be known in prior. This system is of crucial significance to apply and spread the automatic system of vehicle number-plate recognition.

基于相机组网测量的大型结构变形监测技术与应用

传统摄影测量的经过长期发展已经形成较为完善技术框架和理论基础,但是难以突破观测范围及观测物距与测量分辨率及精度之间固有矛盾的限制,在解决大型工程和结构的变形监测过程中多点同步、实时动态、精密测量等问题时面临挑战。针对桥梁等大型结构,本文提出了一种新的摄像测量系统构型方式,能够解决测量范围、测量物距与相机物理分辨率之间的内在冲突,通过桥梁监测实验,证明了相机网络技术能够实现大型工程结构变形的精密动态观测。针对风电叶片监测问题,本文通过全局观测站和局部随动站的相机组网测量,能够同时观测风电叶片的全局运动和局部变形,获得精确的结果。相机组网极大拓展了传统摄像测量的能力和应用前景。

基于卷积神经网络的摄像机姿态感知系统设计

该文设计一种基于卷积神经网络的摄像机姿态感知系统,运用深度学习方法结合传感器技术,获取摄像机实时姿态数据,特别是摄像机运动过程中的姿态数据。系统采用孪生卷积神经网络,通过摄像机采集的环境图像对孪生卷积神经网络进行训练获得摄像机姿态感知模型,在使用时通过将摄像机采集的视频图像输入摄像机姿态感知模型获得摄像机的位姿数据。系统解决可转动式摄像机的实时姿态感知问题,可在公共安全、工厂、交通和矿山等领域广泛推广应用。

《昆明 蒙特利尔全球生物多样性框架》下的生物多样性监测

生物多样性监测是执行和评估《昆明蒙特利尔全球生物多样性保护框架》(简称《昆蒙框架》)长期目标和行动指标所需要的关键数据的主要来源。简要介绍了《昆蒙框架》下的全球生物多样性观测网络(The global biodiversity observation network,GEO BON)概况、中国已有的生物多样性监测/观测网络,构建全球生物多样性观测系统(A global biodiversity observation system,GBiOS)的目的和核心监测指标,分析了生物多样性监测网络建设的一般步骤和GEO BON建议的4种生物多样性监测新方法。提出,地理空间单元与生物类群的全覆盖监测是建设生物多样性监测网络的主要目标,传统监测方法和新监测技术的互补是当前生物多样性监测的重点工作内容。

In-Motes EYE: A Real Time Application for Automobiles in Wireless Sensor Networks

Wireless sensor networks have been identified as one of the key technologies for the 21st century. In order to overcome their limitations such as fault tolerance and conservation of energy, we propose a middleware solution, In-Motes. In-Motes stands as a fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort the deployed applications to run in an energy efficient manner inside the network. The proposed scheme is evaluated through the In-Motes EYE application, aiming to test its merits under real time conditions. In-Motes EYE application which is an agent based real time In-Motes application developed for sensing acceleration variations in an environment. The application was tested in a prototype area, road alike, for a period of four months.