一种利用IMSI检测和人脸识别的手机防盗追踪系统研究

针对手机被盗带来的隐私数据泄露问题,提出了一种利用IMSI(international mobile subscriber identification number)检测和人脸识别的手机防盗追踪系统.针对IMSI检测,给出了一种分层—二分查找的查找方法,并在手机用户数据库上进行了多组对比实验,结果表明该方法具有较高的查找效率.针对人脸识别,提出了一种基于Gabor二值模式和分块加权的单样本人脸识别算法,该算法在FERET人脸数据库上表现出良好的识别性能.通过该系统可以确定被盗手机的位置和非法用户身份信息,提高了找回手机的可能性.     

融合检测和跟踪的实时人脸跟踪

目的 在实时人脸跟踪过程中,因光照变化、目标被遮挡以及跟踪时间长等因素,导致的误差累积都会影响系统的整体性能。针对这些问题,提出一种融合检测和跟踪技术的方法,其中包含了检测、控制和跟踪3个模块(简称DCT)。方法 在检测模块中,利用AdaBoost算法提取人脸的相关信息,并将信息传递给跟踪模块进行跟踪处理;在跟踪模块中,采用在线随机蕨和SURF(speeded up robust features)算法对目标进行跟踪。同时,在每次检测到目标之后,会通过控制模块对当前跟踪目标准确性进行判断。结果 选取国际标准数据组并与LBP+Camshift+Kalman滤波算法、SEMI算法、TLD(tracking-learning-detection)算法比较,实验结果表明,DCT方法在目标发生尺度较大变化、目标遮挡、旋转、形变以及光照发生变化时都具有良好的跟踪识别效果,DCT方法识别准确率在95%以上,平均误识别率和漏识别率分别为0.86%和0.78%。结论 DCT方法具有消除误差累积,跟踪失败后自动恢复等特点,同时可以消除环境中光照、遮挡和仿射变换的影响并满足系统跟踪的实时性要求,运用于视频人脸跟踪系统中能够提高系统的实时性及鲁棒性。     

基于深度学习的ARM平台实时人脸识别

针对ARM平台上人脸识别实时性不强和识别率低的问题，提出一种基于深度学习的实时人脸识别方法。首先基于MTCNN人脸检测算法设计了一种实时检测并追踪人脸的算法；然后在ARM平台上基于深度残差网络（ResNet）设计人脸特征提取网络；最后针对ARM平台的特点，使用Mali-GPU加速人脸特征提取网络的运算，分担CPU负荷，提高系统整体运行效率。算法部署在基于ARM的瑞芯微RK3399开发板上，运行速度达到22 帧/s。实验结果表明，与MobileFaceNet相比，该方法在MegaFace上的识别率提升了11个百分点。     

Development of a vision-based ground target detection and tracking system for a small unmanned helicopter

It is undoubted that the latest trend in the unmanned aerial vehicles (UAVs) community is towards visionbased unmanned small-scale helicopter, utilizing the maneuvering capabilities of the helicopter and the rich information of visual sensors, in order to arrive at a versatile platform for a variety of applications such as navigation, surveillance, tracking, etc. In this paper, we present the development of a visionbased ground target detection and tracking system for a small UAV helicopter. More specifically, we propose a real-time vision algorithm, based on moment invariants and two-stage pattern recognition, to achieve automatic ground target detection. In the proposed algorithm, the key geometry features of the target are extracted to detect and identify the target. Simultaneously, a Kalman filter is used to estimate and predict the position of the target, referred to as dynamic features, based on its motion model. These dynamic features are then combined with geometry features to identify the target in the second-stage of pattern recognition, when geometry features of the target change significantly due to noise and disturbance in the environment. Once the target is identified, an automatic control scheme is utilized to control the pan/tilt visual mechanism mounted on the helicopter such that the identified target is to be tracked at the center of the captured images. Experimental results based on images captured by the small-scale unmanned helicopter, SheLion, in actual flight tests demonstrate the effectiveness and robustness of the overall system.     

基于mean-shift算法的人脸实时跟踪方法*

复杂环境下实时人脸跟踪方法在视觉监控系统中具有很重要的意义,但目前的跟踪算法普遍存在目标遮挡、尺寸变化等过于敏感的不足,限制了其应用范围。提出了一种人脸检测、mean-shift算法与卡尔曼滤波器相结合的实时全自动人脸跟踪算法。实验结果表明该算法实时性很强,可以实现对运动人脸的快速跟踪,同时对目标遮挡也有很好的鲁棒性。     

基于mean-shift算法的人脸实时跟踪方法*

复杂环境下实时人脸跟踪方法在视觉监控系统中具有很重要的意义,但目前的跟踪算法普遍存在目标遮挡、尺寸变化等过于敏感的不足,限制了其应用范围。提出了一种人脸检测、mean-shift算法与卡尔曼滤波器相结合的实时全自动人脸跟踪算法。实验结果表明该算法实时性很强,可以实现对运动人脸的快速跟踪,同时对目标遮挡也有很好的鲁棒性。     

Hyperspectral-cube-based mobile face recognition: A comprehensive review

With the hyperspectral sensor technology evolving and becoming more cost-effective, hyperspectral imaging offers new opportunities for robust face recognition. Hyperspectral face cubes contain much more spectral information than face images from common RGB color cameras. Hyperspectral face recognition is robust to the impacts, such as illumination, pose, occlusion, and spoofing, which can heavily avoid the limitations of the visible-image-based face recognition.In this paper, we summarize the spectrum properties of hyperspectral face cubes and survey the hyperspectral face recognition methods in the literature. We categorize them into major groups for better understanding. We overview the existing hyperspectral face datasets, and establish our own dataset. We also discuss efficient neural networks used for mobile face recognition and conduct experiments on mobile hyperspectral face recognition. Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube-based methods have higher recognition accuracy than visible-image-based methods. Finally, we deliver insightful discussions and prospects for future works on mobile hyperspectral face recognition.     

非完整移动机器人的轨迹跟踪控制

讨论基于运动学模型的非完整移动机器人的轨迹跟踪控制问题。在一定的假设条件下实现了全局指数跟踪，该假设允许参考模型角速度和平移速度均趋于零，并将该方法推广到 动力学模型。仿真例子证明了该方法的有效性。     

EVP描述符在实时人脸识别中的应用

提出了新的EVP（边缘向量模式）人脸描述（或表示）方法,并结合常见分类器组成人脸识别系统,与其他已有方法比较。EVP的特点是不仅用梯度值代替像素强度值描述对象的外观和形状,还描述了邻近区域信息之间的关系。对于FERET人脸数据库的实验结果显示,当结合最近邻分类器时,EVP优于其他表示方法。对于LFW数据库,并结合多核支持向量机（MKL-SVM）的EVP,得到的结果非常令人满意。更可贵的是,基于EVP方法的识别速度大约是基于伽柏（Gabor）方法的20倍。而基于伽柏的方法是目前公认较好的方法。     

基于反步法的移动机器人鲁棒跟踪控制

以四轮移动机器人为研究对象,建立了机器人完整的数学模型,包括运动学模型、动力学模型以及驱动电机模型。在机器人数学模型的基础上,采用反步法的思想设计具有全局收敛特性的鲁棒轨迹跟踪控制器,设计中考虑了驱动电机模型使控制器更符合实际控制要求,并将其分解为运动学控制器、动力学控制器以及电机控制器三部分,降低了控制器设计的难度。构造了系统的李雅普诺夫函数,证明了该类型移动机器人在所得控制器作用下,能实现对给定轨迹的全局渐近追踪。仿真实验结果表明基于反步法的控制器是有效的。     

Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape

This paper proposes a novel framework of real-time face tracking and recognition by combining two eigen-based methods. The first method is a novel extension of eigenface called augmented eigenface and the second method is a sparse 3D eigentemplate tracker controlled by a particle filter. The augmented eigenface is an eigenface augmented by an associative mapping to 3D shape that is specified by a set of volumetric face models. This paper discusses how to make up the augmented eigenface and how it can be used for inference of 3D shape from partial images. The associative mapping is also generalized to subspace-to-one mappings to cover photometric image changes for a fixed shape. A novel technique, called photometric adjustment, is introduced for simple implementation of associative mapping when an image subspace should be combined to a shape. The sparse 3D eigentemplate tracker is an extension of the 3D template tracker proposed by Oka et al. In combination with the augmented eigenface, the sparse 3D eigentemplate tracker facilitates real-time 3D tracking and recognition when a monocular image sequence is provided. In the tracking, sparse 3D eigentemplate is updated by the augmented eigenface while face pose is estimated by the sparse eigentracker. Since the augmented eigenface is constructed on the conventional eigenfaces, face identification and expression recognition are also accomplished efficiently during the tracking. In the experiment, an augmented eigenface was constructed from 25 faces where 24 images were taken in different lighting conditions for each face. Experimental results show that the augmented eigenface works with the 3D eigentemplate tracker for real-time tracking and recognition.     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.     

Pose-invariant face recognition using a 3D deformable model

The paper proposes a novel, pose-invariant face recognition system based on a deformable, generic 3D face model, that is a composite of: (1) an edge model, (2) a color region model and (3) a wireframe model for jointly describing the shape and important features of the face. The first two submodels are used for image analysis and the third mainly for face synthesis. In order to match the model to face images in arbitrary poses, the 3D model can be projected onto different 2D viewplanes based on rotation, translation and scale parameters, thereby generating multiple face-image templates (in different sizes and orientations). Face shape variations among people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by model matching and the system synthesizes face images of known subjects in the same pose. The face is then classified as the subject whose synthesized image is most similar. The synthesized images are generated using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived from training face images of the subject. Experimental results show that the method is capable of determining pose and recognizing faces accurately over a wide range of poses and with naturally varying lighting conditions. Recognition rates of 92.3% have been achieved by the method with 10 training face images per person.     

Unified model in identity subspace for face recognition

Human faces have two important characteristics: (1) They are similar objects and the specific variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information, is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people group A, can be generalized well to facial images of unknown individuals, and can be used to recognize facial images from an unknown people group B with only one sample per subject. Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.     

基于Cesium平台中车辆的碰撞识别跟踪

采用传统方法进行识别与跟踪,效果较差,无法得到精准车辆碰撞数据,提出了基于Cesium平台中车辆的碰撞识别与跟踪。针对车辆碰撞识别需先对样本进行训练,提取出正、负两种样本特征,利用离线车辆碰撞识别分类器识别尺度模块,并在Cesium平台下构建积分图,由此进行图像识别;针对车辆碰撞跟踪需进行阴影检测,根据获得的阴影区,排除非车辆碰撞阴影区,即完成车辆定位与验证。实验结果表明,该方法识别跟踪效果良好。     

多用途人脸识别系统的设计与实现

人脸识别技术是近年来研究的一个热点,众多的人脸识别系统应运而生。提出了一种多用途人脸识别系统,可用作实际环境中智能监控,也可用作实验室环境下的算法测试。该系统整合了人脸检测、正则化、识别等模式识别处理技术,可以在复杂环境下实时、鲁棒地进行人脸识别。系统设计过程中引入了BioAPI国际标准,实现了对相关行业标准的兼容性,便利了算法及系统的实用化。     

基于Gabor小波和模型自适应的鲁棒人脸识别方法*

提出了一种基于Gabor小波人脸特征和模型自适应算法的新鲁棒人脸识别方法。该方法在真实识别前,通过用与真实识别相同的环境条件下所获得的人脸图像数据对原始模型进行更新补偿,实现了模型自适应。该模型自适应更新算法是加性的,其具有较低的时间和空间复杂度。通过模型自适应更新,新方法可以有效地减少模型和识别数据间的失配,从而提高识别率。在AT&T和MITCBCL人脸数据库上的测试结果表明,该方法是有效的。