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.     

Persistent people tracking and face capture using a PTZ camera

Pan–tilt–zoom (PTZ) camera is a powerful tool in far-field scenarios. However, most of the current PTZ surveillance systems require manual intervention to move the camera to the desired position. In this paper, we address the problem of persistent people tracking and face capture in uncontrolled scenarios using a single PTZ camera, which could prove most helpful in forensic applications. The system first detects and tracks pedestrians in zoomed-out mode. Then, according to a scheduler, the system selects a person to zoom in. In the zoomed-in mode, we detect a set of face images and solve the face–face association and face–person association problems. The system then zooms back out where tracking is continued as people re-appear in the view. The person–person association module associates the people on the schedule list with the people in the current view. The detected faces are associated with the corresponding people and trajectories. Due to the dynamic nature of our problem, e.g. the field of view of the camera changes because of the pan/tilt/zoom movement of the camera, all of the processes including receiving images from the camera and processing must be done in real time. To the best of our knowledge, the proposed method is the first to address the association of face images to people and trajectories using a single PTZ camera. Extensive experiments in challenging indoor and outdoor uncontrolled conditions demonstrate the effectiveness of the proposed system.     

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.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.     

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.     

Optimizing PTZ camera calibration from two images

In this paper, we address the problem of calibrating an active pan–tilt–zoom (PTZ) camera. In this regard, we make three main contributions: first, for the general camera rotation, we provide a novel solution that yields four independent constraints from only two images, by directly decomposing the infinite homography using a series of Givens rotations. Second, for a camera varying its focal length, we present a solution for the degenerate cases of pure pan and pure tilt that occur very frequently in practical applications of PTZ cameras. Third, we derive a new optimized error function for pure rotation or pan–tilt rotation, which plays a similar role as the epipolar constraint in a freely moving camera, in terms of characterizing the reprojection error of point correspondences. Our solutions and analysis are thoroughly validated and tested on both synthetic and real data, whereby the new geometric error function is shown to outperform existing methods in terms of accuracy and noise resilience.     

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。     

Dynamic Calibration of Pan–Tilt–Zoom Cameras for Traffic Monitoring

Pan–tilt–zoom (PTZ) cameras have been widely used in recent years for monitoring and surveillance applications. These cameras provide flexible view selection as well as a wider observation range. This makes them suitable for vision-based traffic monitoring and enforcement systems. To employ PTZ cameras for image measurement applications, one first needs to calibrate the camera to obtain meaningful results. For instance, the accuracy of estimating vehicle speed depends on the accuracy of camera calibration and that of vehicle tracking results. This paper presents a novel calibration method for a PTZ camera overlooking a traffic scene. The proposed approach requires no manual operation to select the positions of special features. It automatically uses a set of parallel lane markings and the lane width to compute the camera parameters, namely, focal length, tilt angle, and pan angle. Image processing procedures have been developed for automatically finding parallel lane markings. Interesting experimental results are presented to validate the robustness and accuracy of the proposed method.     

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.     

恒定光照下基于LFPL的APTZ调节

为提高光照恒定情况下视觉系统中PTZ调节的主动性和稳定性,提出恒定光照下基于LFPL的APTZ调节方法。采用基于局部粒子滤波的目标预定位方法,对运动目标实现自动估计的标定,提高系统调节的主动性,解决非线性跟踪问题。动态选取光照不变特征滤波粒子克服了光照变化和噪声等因素对目标预定位方法的影响,增强视觉系统的鲁棒性。对水平角和抑角采用Fuzzy控制方法,提高视觉跟踪系统的稳定性。实验结果表明,该方法是正确有效的,使用该系统对变速运动目标的长距离跟踪结果较传统方法更稳定,在光照变化和噪声条件下的运动目标跟踪实验也取得较好的结果。     

Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera

Abstract. This paper proposes a novel tracking strategy that can robustly track a person or other object within a fixed environment using a pan, tilt, and zoom camera with the help of a pre-recorded image database. We define a set of camera states which is sufficient to survey the environment for the target. Background images for these camera states are stored as an image database. During tracking, camera movements are restricted to these states. Tracking and segmentation are simplified, as each tracking image can be compared with the corresponding pre-recorded background image. Received: 26 August 1999 / Accepted: 22 February 2000     

Dynamic 3D surface reconstruction and motion modeling from a pan–tilt–zoom camera

3D surface reconstruction and motion modeling has been integrated in several industrial applications. Using a pan–tilt–zoom (PTZ) camera, we present an efficient method called dynamic 3D reconstruction (D3DR) for recovering the 3D motion and structure of a freely moving target. The proposed method estimates the PTZ measurements to keep the target in the center of the field of view (FoV) of the camera with the same size. Feature extraction and tracking approach are used in the imaging framework to estimate the target's translation, position, and distance. A selection strategy is used to select keyframes that show significant changes in target movement and directly update the recovered 3D information. The proposed D3DR method is designed to work in a real-time environment, not requiring all frames captured to be used to update the recovered 3D motion and structure of the target. Using fewer frames minimizes the time and space complexity required. Experimental results conducted on real-time video streams using different targets to prove the efficiency of the proposed method. The proposed D3DR has been compared to existing offline and online 3D reconstruction methods, showing that it uses less execution time than the offline method and uses an average of 49.6% of the total number of frames captured.     

Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems

In this paper, we present a novel approach for constructing a large-scale range panoramic background model that provides fast registration of the observed frame and localizes the foreground targets with arbitrary camera direction and scale in a Pan–tilt–zoom (PTZ) camera-based surveillance system. Our method consists of three stages. (1) In the first stage, a panoramic Gaussian mixture model (PGMM) of the PTZ camera’s field of view is generated off-line for later use in on-line foreground detection. (2) In the second stage, a multi-layered correspondence ensemble is generated off-line from frames captured at different scales which is used by the correspondence propagation method to register observed frames online to the PGMM. (3) In the third stage, foreground is detected and the PGMM is updated. The proposed method has the capacity to deal with the PTZ camera’s ability to cover a wide field of view (FOV) and large-scale range. We demonstrate the advantages of the proposed PGMM background subtraction method by incorporating it with a tracking system for surveillance applications.     

基于全景视觉的监控系统设计

设计了一个基于全景视觉的多摄像机监控网络。全景相机视野广,可以实现大范围的目标检测与跟踪。云台摄像机视角具有一定的自由度,可以捕捉目标的高分辨率图像。将全景相机与云台相机相互配合,通过多传感器的数据融合,分层次的跟踪算法及多相机调度算法,实现了大范围的多个运动目标的检测与跟踪,并能捕获目标的清晰图像。实验验证了该系统的有效性和合理性。     

Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks

Pan–tilt–zoom (PTZ) camera networks have an important role in surveillance systems. They have the ability to direct the attention to interesting events that occur in the scene. One method to achieve such behavior is to use a process known as sensor slaving: one (or more) master camera monitors a wide area and tracks moving targets so as to provide the positional information to one (or more) slave camera. The slave camera can thus point towards the targets in high resolution.In this paper we describe a novel framework exploiting a PTZ camera network to achieve high accuracy in the task of relating the feet position of a person in the image of the master camera, to his head position in the image of the slave camera. Each camera in the network can act as a master or slave camera, thus allowing the coverage of wide and geometrically complex areas with a relatively small number of sensors.The proposed framework does not require any 3D known location to be specified, and allows to take into account both zooming and target uncertainties. Quantitative results show good performance in target head localization, independently from the zooming factor in the slave camera. An example of cooperative tracking approach exploiting with the proposed framework is also presented.     

Optimal Representative Blocks for the Efficient Tracking of a Moving Object

Optimal representative blocks are proposed for an efficient tracking of a moving object and it is verified experimentally by using a mobile robot with a pan‐tilt camera. The key idea comes from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the camera of mobile robot and the moving object, the tracking performance of a moving object can be improved by shrinking the size of representative blocks according to the object image size. Motion estimation using edge detection (ED) and block‐matching algorithm (BMA) are often used in the case of moving object tracking by vision sensors. However, these methods often miss the real‐time vision data since these schemes suffer from the heavy computational load. To overcome this problem and to improve the tracking performance, the optimal representative block that can reduce a lot of data to be computed is defined and optimized by changing the size of the representative block according to the size of object in the image frame. The proposed algorithm is verified experimentally by using a mobile robot with a two degree‐of‐freedom active camera. © 2004 Wiley Periodicals, Inc.     

PTZ摄像机跟踪运动目标的智能控制算法的研究

针对传统的PTZ摄像机跟踪运动目标时依靠人工操作,无法连续、实时动态跟踪,甚至导致跟踪失败的缺点,提出以HSV颜色直方图作为模型特征,通过Camshift算法和卡尔曼滤波器实现运动目标的定位和预测补偿,运用闭环控制机制自动调节云台的转动和镜头的变倍,提高了系统的实时性。通过Android智能手机手动调节云台和镜头,配合自动跟踪系统,使跟踪效果更准确。结果表明:该方法是可行的,具有控制简单、定位准确的优点,能提高目标跟踪的实时性和可靠性。     

Facial Motion Analysis for Content-based Video Coding

Automatic wire-frame fitting and automatic wire-frame tracking are the two most important and most difficult issues associated with semantic-based moving image coding. A novel approach to high speed tracking of important facial features is presented as a part of a complete fitting-tracking system. The method allows real-time processing ofhead-and-shoulders sequences using software tools only. The algorithm is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Each important facial feature (the left eye, the right eye, the nose and the lips) is tracked separately using the same method. The algorithm was tested on widely used head-and-shoulders video sequences containing the speaker's head pan, rotation and zoom with remarkably good results. These experiments prove that it is possible to maintain tracking even when the facial features are partially occluded.     

基于广义关联聚类图的分层关联多目标跟踪

检测跟踪是近期多目标跟踪研究的热点方向之一.目前大部分方法都是基于相邻帧之间的双向匹配,对检测点进行数据融合.本文提出的方法是,给定一个滑动时间窗口,在窗口内对某个目标每帧出现的检测点进行一次性数据融合.我们把多目标跟踪看作图的分割问题,利用广义关联聚类（Generalized correlation clustering problem,GCCP）图优化文中提出的数据融合.吸取分层数据关联的思想,把多目标跟踪分成两个阶段.首先,在时间窗口内遵循检测点,利用广义关联聚类,得到自适应长度的轨迹片段,轨迹片段长度不受窗口宽度的限制.然后,基于轨迹片段进一步数据关联,得到目标的长轨迹.在公共数据集上的实验测试表明,本文方法能够有效地实现多目标跟踪,对于遮挡处理、身份转换处理以及轨迹的生成具有很好的鲁棒性,多目标跟踪准确率（Multiple object tracking accuracy,MOTA）超过当前水平.