一种用于视频监控的双摄像头系统实现

提出了一种新颖的用于视频监控的双摄像头系统,在此系统中全景摄像机与PTZ摄像机(云台摄像机)结合在一起,既能对大范围内的目标进行检测与跟踪又能对目标的详细图像进行捕捉。在全景摄像机获取的图像中进行运动检测,获取运动物体位置信息后利用PTZ摄像机对其进行检测分析,以实现二者的数据融合。设计了全景摄像机的反射镜面,对该双摄像头系统进行了标定,在实验室环境下的进行实验验证了系统的性能。     

仿生复眼式全景探测及跟踪策略

为解决传统相机不能在全景范围内快速连续跟踪运动物体的弊端,本文提出一种仿生复眼式全景探测思想,以及配套的跟踪策略。在硬件方面,采用多个子眼相机进行全景实时探测,中心采用一个大孔径主眼相机,并且将其安装于云台上面,通过云台控制器接收的不同指令码进行精确定位。在软件方面,本文采用自动加窗采集的思想,对于运动区域的图像进行多层窗口采集,非运动区域则减少窗口层数,然后将采集的图像进行高斯运动目标检测,而检测方法本文在重叠区采用了仿生复眼的侧抑制算法,得到了比较清晰的运动目标轮廓。按照上述软硬件思想本文搭建了实际实验装置,并且进行了重复性实验。由于实时探测特点,以及加窗采集和侧抑制算法的结合,相比传统相机的跟踪效果,在视场和灵敏度上得到了很大的提高。     

一种基于特征匹配的目标入侵检测方法

视频监控中常用云台摄像机监控视场较大的区域.对于云台摄像机跟随拍摄的情况,提出了一种基于特征匹配的目标入侵检测方法.通过提取的尺度不变特征变换(SIFT)特征点对,将当前图像和全景图像进行匹配,从而得到当前图像和全景图像投影关系,再将当前图像的坐标系变换到全景图像下,最后运用差分法,找到入侵目标.实验结果表明,即使当前图像与全景图像存在尺度、缩放、形变等差异,通过本方法也可正确地检测出入侵目标.     

小型无人机地面目标跟踪系统机载云台自适应跟踪控制

本文针对小型无人机地面目标跟踪系统,提出了一种机载云台自适应跟踪控制算法.该算法在摄像机外参数未知的情况下,利用图像信息和机载传感器得到的无人机状态进行反馈,最终实现了对云台摄像机姿态的控制,使得无人机在跟踪目标飞行过程中,地面目标可以始终保持在摄像机的图像中心.为此,论文首先通过分析无人机、目标和摄像机三者的相对位姿关系,建立了目标点在图像平面的运动学模型.在此基础上,基于李雅普诺夫稳定性理论设计了自适应控制算法.理论分析与仿真结果表明本文所设计的摄像机姿态控制器在摄像机外参数未知的情况下,可以使被跟踪目标始终保持在图像中心.     

双摄像机协同人脸鹰眼检测与定位方法

现代视频监控系统需要获取大范围场景中感兴趣目标的清晰图像,这在目标距离较远并且不断移动时单纯采用摄像机调焦方式通常有一定的困难。为了获取宽范围监控场景中远距离行人的主要面部特征,采用广角静止—窄视场运动双摄像机协同工作方式可以同时获得远距离目标的全局和细节信息。首先采用改进的Codebook背景减法从广角摄像机中检测运动目标,然后指引运动摄像机近距离跟踪观察;若行人停止运动,则利用运动摄像机对其进行放大,然后从中检测人脸,并将人脸置于视野中心放大得到清晰图像。当行人再次运动时,广角相机将初始位置再次传递给运动摄像机,由其再对行人进行跟踪。通过实验室内和室外真实场景的实验表明,广角相机的检测算法具有一定的鲁棒性,运动相机能跟踪放大行人人脸图像,算法运行速度满足实时性要求。     

动态云台摄像机无人机检测与跟踪算法

为应对小型无人机的黑飞、滥飞对个人隐私、公共安全造成的威胁,本文采用高清云台摄像机定点巡航的方式对近地动态复杂背景中的无人机进行检测与跟踪,并提出了一种适用于动态云台摄像机的闭环无人机检测与跟踪算法,包含检测与跟踪两种模式。在检测模式下,本文设计了一种基于运动背景补偿的运动目标检测算法来提取分类候选区域,然后利用基于神经网络结构搜索得到的轻量级卷积神经网络对候选区域进行分类识别,可在不缩小高清视频图像的条件下实现无人机检测;在跟踪模式下,本文提出了一种结合卡尔曼滤波的局部搜索区域重定位策略改进了核相关滤波跟踪算法,使之在高清云台伺服追踪过程中仍能对目标进行快速稳定的跟踪;为将检测模式与跟踪模式结合在闭环框架中,本文还提出了一种基于检测概率和跟踪响应图状态的自适应检测与跟踪切换机制。实验表明,本文算法可应用于定点巡航状态的高清云台摄像机,实现近地复杂动态背景中无人机的实时准确检测、识别与快速跟踪。     

全景相机与PTZ相机联动监控系统及位置关联模型

为实现大范围监视和指定区域精细观测与分析,提出一种全景相机与PTZ相机组合的联合监控系统.通过分析两相机安装的各种位置关系,建立起了统一位置关联模型.利用所建立的位置关联模型可方便地对监视场景的目标位置从全景图像到PTZ相机图像转换,为实现场景目标的联合监控奠定基础.实验结果表明,利用该模型所计算的水平、俯仰坐标相对于其实际测量值的平均误差均小于云台相机的实际控制误差,满足系统的联动控制要求.     

全景相机与PTZ相机联动监控系统及位置关联模型

为实现大范围监视和指定区域精细观测与分析,提出一种全景相机与PTZ相机组合的联合监控系统.通过分析两相机安装的各种位置关系,建立起了统一位置关联模型.利用所建立的位置关联模型可方便地对监视场景的目标位置从全景图像到PTZ相机图像转换,为实现场景目标的联合监控奠定基础.实验结果表明,利用该模型所计算的水平、俯仰坐标相对于其实际测量值的平均误差均小于云台相机的实际控制误差,满足系统的联动控制要求.     

基于多CPU的目标识别与跟踪系统设计

设计了一个实时图像采集、目标识别与跟踪系统;系统功能包括图像采集、运动估计、目标识别、摄像机云台控制等;系统功能主要由3片CPU完成,其中由2片DSP构成的图像采集处理单元实现两路图像采集、目标识别和运动估计,识别和估计结果通过系统总线送给跟踪控制系统;由1片ARM构成的目标跟踪控制系统对2路摄像机的云台和镜头等实现实时控制,使目标一直位于视场中;该系统采样率为10fps,当运动目标的距离在100 m以内,速度小于15 m/s时,可以实现实时识别与跟踪;该系统可以应用于视频监控等应用领域。     

基于图像采集卡的智能安防监控系统设计

本文研究了基于Matrox图像采集卡的智能安防监控系统的总体解决方案,硬件设计部分详细叙述了该系统的图像采集模块设计和云台控制模块设计.通过对摄像机采集的图像进行运动目标的跟踪分析.产生控制信号,控制承栽摄像头的云台运动.以实现运动目标的跟踪:软件设计部分.针对运动目标的检测跟踪各种跟踪方法,详细研究并实现了基于时间差分的运动目标检测方法.完成了系统整体软件设计.实践证明,该系统具有较好的检测效果.     

A Catadioptric and Pan-Tilt-Zoom Camera Pair Object Tracking System for UAVs

Unmanned aerial vehicles (UAVs) are seeing widespread use in military, scientific, and civilian sectors in recent years. As the mission demands increase, these systems are becoming more complicated. Omnidirectional camera is a vision sensor that can captures 360° view in a single frame. In recent years omnidirectional camera usage has experienced a remarkable increase in many fields, where many innovative research has been done. Although, it is very promising, employment of omnidirectional cameras in UAVs is quite new. In this paper, an innovative sensory system is proposed, that has an omnidirectional imaging device and a pan tilt zoom (PTZ) camera. Such a system combines the advantages of both of the camera systems. The system can track any moving object within its 360° field of view and provide detailed images of it. The detection of the moving object has been accomplished by an adaptive background subtraction method implemented on the lowered resolution images of the catadioptric camera. A novel algorithm has also been developed to estimate the relative distance of the object with respect to the UAV, using tracking information of both of the cameras. The algorithms are implemented on an experimental system to validate the approach.     

Surveillance camera scheduling: a virtual vision approach

We present a surveillance system, comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom (PTZ) active cameras, which automatically captures high-resolution videos of pedestrians as they move through a designated area. A wide-FOV static camera can track multiple pedestrians, while any PTZ active camera can capture high-quality videos of one pedestrian at a time. We formulate the multi-camera control strategy as an online scheduling problem and propose a solution that combines the information gathered by the wide-FOV cameras with weighted round-robin scheduling to guide the available PTZ cameras, such that each pedestrian is observed by at least one PTZ camera while in the designated area. A centerpiece of our work is the development and testing of experimental surveillance systems within a visually and behaviorally realistic virtual environment simulator. The simulator is valuable as our research would be more or less infeasible in the real world given the impediments to deploying and experimenting with appropriately complex camera sensor networks in large public spaces. In particular, we demonstrate our surveillance system in a virtual train station environment populated by autonomous, lifelike virtual pedestrians, wherein easily reconfigurable virtual cameras generate synthetic video feeds. The video streams emulate those generated by real surveillance cameras monitoring richly populated public spaces.A preliminary version of this paper appeared as [1].     

An efficient continuous tracking system in real-time surveillance application

A tracking object must present a proper field of view (FOV) in a multiple active camera surveillance system; its clarity can facilitate smooth processing by the surveillance system before further processing, such as face recognition. However, when pan–tilt–zoom (PTZ) cameras are used, the tracking object can be brought into the FOV by adjusting its intrinsic parameters; consequently, selection of the best-performing camera is critical. Performance is determined by the relative positions of the camera and the tracking objects, image quality, lighting and how much of the front side of the object faces the camera. In a multi-camera surveillance system, both camera hand-off and camera assignment play an important role in automated and persistent tracking, which are typical surveillance requirements. This study investigates the use of automatic methods for tracking an object across cameras in a surveillance network using PTZ cameras. An automatic, efficient continuous tracking scheme is developed. The goal is to determine the decision criteria for hand-off using Sight Quality Indication (SQI) (which includes information on the position of the object and the proportion of the front of object faces the camera), and to perform the camera handoff task in a manner that optimizes the vision effect associated with monitoring. Experimental results reveal that the proposed algorithm can be efficiently executed, and the handoff method for feasible and continuously tracking active objects under real-time surveillance.     

An adaptive focus-of-attention model for video surveillance and monitoring

In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.     

基于FPGA的拼接式CMOS线阵相机系统设计

针对单个线阵相机在大视场下传感器边缘失光的问题,提出了在保证分辨率的前提下使用多个相对较低像素的相机拼接,对视场进行分担的方法;设计了一种基于FPGA的拼接式CMOS线阵相机系统,系统整体像素为4 096Pixels,行频为2.5kHz;详细介绍了系统的工作原理、硬件构成和各芯片的程序设计,实现了基于FPGA的多相机图像实时校正算法,并将校正后的图像通过高速USB芯片传输到采集软件;实验结果表明,在视场较大的条件下同等像素的拼接式相机相比单相机在视场边缘具有更好的成像质量,且需要的物距更短,可应用于对多离散目标的检测。     

Systems and algorithms for autonomous and scalable crowd surveillance using robotic PTZ cameras assisted by a wide-angle camera

We report an autonomous surveillance system with multiple pan-tilt-zoom (PTZ) cameras assisted by a fixed wide-angle camera. The wide-angle camera provides large but low resolution coverage and detects and tracks all moving objects in the scene. Based on the output of the wide-angle camera, the system generates spatiotemporal observation requests for each moving object, which are candidates for close-up views using PTZ cameras. Due to the fact that there are usually much more objects than the number of PTZ cameras, the system first assigns a subset of the requests/objects to each PTZ camera. The PTZ cameras then select the parameter settings that best satisfy the assigned competing requests to provide high resolution views of the moving objects. We propose an approximation algorithm to solve the request assignment and the camera parameter selection problems in real time. The effectiveness of the proposed system is validated in both simulation and physical experiment. In comparison with an existing work using simulation, it shows that in heavy traffic scenarios, our algorithm increases the number of observed objects by over 210%.     

Continuous localization and mapping of a pan–tilt–zoom camera for wide area tracking

Pan–tilt–zoom (PTZ) cameras are well suited for object identification and recognition in far-field scenes. However, the effective use of PTZ cameras is complicated by the fact that a continuous online camera calibration is needed and the absolute pan, tilt and zoom values provided by the camera actuators cannot be used because they are not synchronized with the video stream. So, accurate calibration must be directly extracted from the visual content of the frames. Moreover, the large and abrupt scale changes, the scene background changes due to the camera operation and the need of camera motion compensation make target tracking with these cameras extremely challenging. In this paper, we present a solution that provides continuous online calibration of PTZ cameras which is robust to rapid camera motion, changes of the environment due to varying illumination or moving objects. The approach also scales beyond thousands of scene landmarks extracted with the SURF keypoint detector. The method directly derives the relationship between the position of a target in the ground plane and the corresponding scale and position in the image and allows real-time tracking of multiple targets with high and stable degree of accuracy even at far distances and any zoom level.     

监控系统中的多摄像机协同

描述了一个用于室内场合对多个目标进行跟踪的分布式监控系统.该系统由多个廉价的固定镜头的摄像机构成,具有多个摄像机处理模块和一个中央模块用于协调摄像机间的跟踪任务.由于每个运动目标有可能被多个摄像机同时跟踪,因此如何选择最合适的摄像机对某一目标跟踪,特别是在系统资源紧张时,成为一个问题.提出的新算法能根据目标与摄像机之间的距离并考虑到遮挡的情况,把目标分配给相应的摄像机,因此在遮挡出现时,系统能把遮挡的目标分配给能看见目标并距离最近的那个摄像机.实验表明该系统能协调好多个摄像机进行目标跟踪,并处理好遮挡问题.     

Camera handoff with adaptive resource management for multi-camera multi-object tracking

Camera handoff is a crucial step to obtain a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are identified across different cameras. However, there exist many unsolved questions in developing an efficient camera handoff algorithm. In this paper, we first design a trackability measure to quantitatively evaluate the effectiveness of object tracking so that camera handoff can be triggered timely and the camera to which the object of interest is transferred can be selected optimally. Three components are considered: resolution, distance to the edge of the camera’s field of view (FOV), and occlusion. In addition, most existing real-time object tracking systems see a decrease in the frame rate as the number of tracked objects increases. To address this issue, our handoff algorithm employs an adaptive resource management mechanism to dynamically allocate cameras’ resources to multiple objects with different priorities so that the required minimum frame rate is maintained. Experimental results illustrate that the proposed camera handoff algorithm can achieve a substantially improved overall tracking rate by 20% in comparison with the algorithm presented by Khan and Shah.