AFRS人脸自动识别系统的设计与开发

人脸检测、跟踪和识别技术是人机自然交互领域中一个富有挑战性的课题,也是近年来研究的一个热点。在新一代(第四代)人机自然交互系统中人脸自动识别系统提供了一个友好的人机交互接口。本文着重介绍了AFRS人脸自动识别系统的设计思想和实现技术。该系统以实现一个实时的、鲁棒的、自动化的系统为目标,融合了人脸检测、跟踪和识别技术以及其它的图像和视频处理技术,并且建立和维护了一个含有人脸特征数据的数据库,从而使得系统可以自动地对进入系统监控范围内的人脸自动进行检测、跟踪和识别。该系统在新一代的人机交互、安保、视频监控等方面有着广泛的应用前景。     

XYZ视频图像压缩编码

本文讨论并实现了基于3-D DCT的XYZ视频图像压缩编码方法.该方法不涉及运动估计和运动补偿,复杂度低.实验结果表明,这种算法能快速地、高质量地压缩视频图像,是一种很有潜力的视频编码方法.     

Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning

A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces; (b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return; (c) run with a high computation efficiency, run at about 20 frames/s.     

基于包容式结构的智能循迹小车设计

设计出一个基于包容式结构的智能循迹小车,该小车采用单片机AT89S51作为控制器。该系统主要有避障行为模块、轨线跟踪行为模块、远程控制行为以及紧急停车模块。采用红外传感器设计检测轨线模块,超声波传感器设计避障模块。对于避障行为模块,提出采用模糊控制算法进行避障。经大量实验验证该小车不仅能稳定跟踪轨线,而且能绕障碍物行走。     

基于OpenMV的四旋翼无人机目标跟踪系统设计与实现

基于四旋翼无人机小巧便捷可控的特点,设计并实现了一种可识别并自动跟踪物体的四旋翼无人机系统,该系统采用STM32F407为主控芯片,综合光流模块、数传模块、OpenMV4模块和电机、螺旋桨等外围设备,采用姿态估算、色块跟踪解耦合、色块识别、滤波、坐标系转换、PID调节、前馈调节等算法实现四旋翼的识别与自动跟踪,使其可以在平稳飞行的同时,按照程序指令搜寻目标,并触发跟踪状态,完成自动跟踪。该四旋翼无人机系统灵巧便携,操作简单,通用性强,应用前景广阔。     

一种快速多人脸跟踪算法

提出一个基于肤色的快速多人脸跟踪算法.利用多个CAMShift跟踪器实现多人脸跟踪,提出最优排序法和目标消除法解决多人脸跟踪过程中目标发生粘连重叠的问题;引入多辅助信息和表决制解决了相邻两帧中人脸的对应问题.为进一步提高整个算法的跟踪速度和鲁棒性,引入卡尔曼滤波器对目标进行预测.实验结果表明,该算法可实时稳健地实现多人脸跟踪.     

人脸检测跟踪与特征点定位系统

实现的人脸检测跟踪与特征点定位系统,基于VC++6.0开发平台,使用opencv作为开发工具,有效缩短了系统的开发时间。首先,本系统采用adaboost算法进行人脸检测,通过合理的特征模板的选择实现了人脸的实时检测;其次,人脸跟踪模块选用camshift算法,利用人脸检测模块生成的人脸坐标传递给跟踪模块,实现人脸的自动实时跟踪,同时建立多个camshift跟踪器对多人脸进行跟踪,并有效地解决了人脸遮挡的问题;最后,通过ASM(active shapemodel)算法实现了实时人脸特征点定位。实验结果表明该系统实现的人脸实时检测跟踪及特征点定位,效果明显,可以作为表情分析和情感计算、视频人脸识别开发的基础。     

Combining passive visual cameras and active IMU sensors for persistent pedestrian tracking

Vision based pedestrian tracking becomes a hard problem when long-term/heavy occlusion happens or pedestrian temporarily moves out of the visual field. In this paper, a novel persistent pedestrian tracking system is presented which combines visual signal from surveillance cameras and sensor signals from Inertial Measurement Unit (IMU) carried by pedestrians themselves. IMU tracking performs Dead Reckoning (DR) approach utilizing accelerometer, gyroscope and magnetometer. IMU tracking has nothing to do with visual occlusion, so it keeps working even when pedestrians are visually occluded. Meanwhile, visual tracking assists in calibrating IMU to avoid the bias drift during DR. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robust pedestrian tracking in many challenging scenarios.     

Robust face tracking via collaboration of generic and specific models.

Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background.     

Audio–Video based People Counting and Security Framework for Traffic Crossings

In this paper, we propose a traffic management system which uses both audio and video data at intersections to prevent traffic congestion. A novel scheme of counting and tracking crowd with a single overhead camera is proposed for the purpose of real-time traffic control at crossings as well as for adjusting the carrier frequency using the video data. The system recognizes people movement along various directions estimating the possibility of traffic congestion. To carry out the tracking procedure, various temporal and spatial features of the images are used to identify the people in the crowd in order to predict the position of the objects in the current frame. Several issues such as emergence of people, departure of people, occlusions, and de-occlusions are resolved through interactions between regions and objects. With the help of audio data at counting region, we identify children by their voice using pitch analysis. The database constitutes of voices of speakers of all ages and of both genders. The paper also includes automated accident detection at the traffic intersections through use of audio data at intersections. It can classify the sounds into “crash”, “power brake” and “normal traffic” sounds. The experimental results show the effectiveness of our framework.

多外观模型的鲁棒人脸跟踪

为了解决在真实场景中进行视觉人脸跟踪时不同挑战之间的外观匹配问题,提出了一种多外观模型的人脸跟踪算法。该算法利用多个具有长期和短期外观记忆的外观模型进行有效的人脸跟踪,对变形、旋转、尺度和光照变化表现出鲁棒性。同时利用先检测后跟踪算法的优点,通过使用人脸检测器来处理人脸的剧烈外观变化,检测器也有助于在漂移过程中重新初始化所提算法。最后提出了一种加权分数级融合策略,通过在可能的人脸位置生成的候选人脸来获得融合值最高的人脸跟踪输出。实验结果证明,该跟踪器在自启动时表现出色,性能优于许多先进的跟踪器。     

融合GMS检测和置信度判别的TLD目标跟踪

针对目标在遮挡、尺度变化等复杂场景下易产生模型漂移问题,基于跟踪学习检测(TLD)框架提出一种结合基于网格的运动统计(GMS)检测和置信度判别的长时目标跟踪算法.首先在跟踪模块中采用快速判别尺度空间的相关滤波器(fDSST)作为跟踪器,利用位置滤波器和尺度滤波器对上一帧目标进行位置与尺度的判别,并依据TLD算法中跟踪模...     

基于OpenCV的双摄像头活体人脸判定及实时跟踪系统

活体检测及人脸跟踪系统的构建已成为现代计算机视觉及OpenCV研究的热点。提出一种基于OpenCV的双摄像头活体人脸判定实时人脸跟踪系统。一种新的双摄像头活体人脸判定的方法,利用基于HAAR特征人脸检测算法,并判断距离。编写Windows程序调用串口API和下位单片机STC12C5410通信,采用CH341A芯片将RS232串口数据加载到USB协议数据栈中实现USB传输,单片机控制FUTABA舵机,获取上位机人脸检测坐标信息,实时调整舵机转动角度,使人脸始终处于图像中间290＊310阈值范围内,达到人脸跟踪。该系统结构精简,可作为模块用于门禁安保系统及构建其他机器人研究平台。     

Face detection in video based on AdaBoost algorithm and skin model

A real-time method is proposed to detect faces in videos. First it uses frame difference method to extract the motion area. Next the clustering character of skin is used to get the general face area. AdaBoost algorithm is applied to make concrete detection of human face. Finally an improved CamShift algorithm method is used to keep the tracking and improving algorithm speed. The experiments demonstrate the robustness and high speed of the proposed algorithm.     

基于时空上下文和随机森林的人眼跟踪定位算法研究

为了得到人眼跟踪过程中更好的鲁棒性和实时性以及跟踪精度,提出一种基于自适应增强分类算法(AdaBoost)、随机森林(RF)和时空上下文(STC)的重定位跟踪算法。该算法结构分为3层,分别为AdaBoost人脸检测、STC人脸跟踪和RF人眼定位。首先,利用AdaBoost在第一帧识别出人脸,从而提取出人脸窗口。接着,使用时空上下文跟踪算法进行人脸跟踪。然后,联合定向梯度直方图(HOG)算法进行相似度判断,以达到目标丢失后继续跟踪的目的。最后,采用随机森林算法进行人眼定位。实验结果表明,与传统的随机森林人眼跟踪算法相比,该算法在跟踪速度达到原方法的2倍左右,并在跟踪精度和鲁棒性上和原算法相同。基本满足在裸眼3D显示时人脸跟踪和人眼定位的精度高、实时性快、鲁棒性好的要求。     

基于Nios II软核的人脸目标实时跟踪系统

提出采用低功耗芯片EP3C40为核心,结合Nios II软核处理器的高处理性能,实现人脸视频信号采集与跟踪,并将相关检测跟踪算法移植于Nios II处理器协同工作的方式实现整体系统的工作。实验结果表明,在环境微弱变化以及人脸不同姿态下,系统都能检测并准确跟踪,同时硬件可重构能力强,算法简单、快速、鲁棒性强。     

基于人脸跟踪和公钥密码的远程在线考试系统

针对现有的现代远程教育在线考试系统中容易出现的考试替考、考试纠纷和网络攻击等诸多问题,提出了一种基于人脸识别跟踪和公钥密码体系的现代远程教育在线考试系统。该系统可以通过使用人脸识别跟踪技术解决考试替考问题,通过数字签名技术解决考试纠纷问题,通过公钥密码学技术防止网络传输差错并解决网络攻击问题。理论分析和实验验证说明,该系统能够有效解决考试替考、考试纠纷和网络攻击等问题,从而为远程教育在线考试系统的大规模应用提供了技术保障。     

A new head pose tracking method based on stereo visual SLAM

Real-time and reliable head pose tracking is the basis of human–computer interaction and face analysis applications. Aiming at the problems of accuracy and real time performance in current tracking method, a new head pose tracking method based on stereo visual SLAM is proposed in this paper. The sparse head map is constructed based on ORB feature points extraction and stereo matching, then the 3D-2D matching relations between 3D mappoints and 2D feature points are obtained by projection matching. Finally, the camera pose solved by the Bundle Adjustment is converted to head pose, which realizes the tracking of head pose. The experimental results show that this method can obtain high precise head pose. The mean errors of three Euler angles are all less than 1°. Therefore, the proposed head pose tracking method can track and estimate precise head pose in real time under smooth background.     

An automatic algorithm for semantic object generation and temporal tracking

Automatic semantic video object extraction is an important step for providing content-based video coding, indexing and retrieval. However, it is very difficult to design a generic semantic video object extraction technique, which can provide variant semantic video objects by using the same function. Since the presence and absence of persons in an image sequence provide important clues about video content, automatic face detection and human being generation are very attractive for content-based video database applications. For this reason, we propose a novel face detection and semantic human object generation algorithm. The homogeneous image regions with accurate boundaries are first obtained by integrating the results of color edge detection and region growing procedures. The human faces are detected from these homogeneous image regions by using skin color segmentation and facial filters. These detected faces are then used as object seed for semantic human object generation. The correspondences of the detected faces and semantic human objects along time axis are further exploited by a contour-based temporal tracking procedure.     

Face detection and tracking using a Boosted Adaptive Particle Filter

A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (APF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate estimates of the proposal distribution and the posterior distribution than the standard Particle Filter thus enabling more accurate tracking in video sequences. In the proposed BAPF algorithm, the AdaBoost algorithm is used to detect faces in input image frames, whereas the APF algorithm is designed to track faces in video sequences. The proposed BAPF algorithm is employed for face detection, face verification, and face tracking in video sequences. Experimental results show that the proposed BAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios.