Navigation :EXPO21XX>愿景21xx>H15：研究与大学>牛津大学
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  • 提供个人资料

  • 主动视觉小组试图促进计算视觉的知识，尤其是在移动对象的检测和跟踪领域，以及从校准和部分校准的图像中恢复的结构恢复。

    The Group works on applications for surveillance, wearable and assistive computing, cognitive vision, augmented reality, human motion analysis, teleoperation, and navigation.
产品介绍

  • 智能监视

  • 这项研究将活动识别的领域与主动传感整合在一起。特定的重点在于数据采集过程以不同级别的分辨率和积极感应的融合，其中包括更高级别的推理。

    接下来描述的主题将在视觉跟踪，活动识别以及对Pan/Tilt/Zoom设备的智能控制中汇集在一起​​，以便能够推论视觉场景，推断因果关系并检测异常或有趣的行为。

    头部姿势的行为
    这个项目的目的是自动identify the direction in which people are facing from a distant camera in a surveillance situation to provide input to higher level reasoning systems. The direction in which somebody is facing provides a good estimate of their gaze direction, which can be used to infer familiarity between people or interest in surroundings. It can be seen as closing the gap between a coarse description of humans from a distance and a more detailed motion of limbs, usually obtained from a closer view. The work is partly funded by HERMES, located in work package 3 and 4.

    积极的场景探索
    Effective use of resources is an underlying theme of this project. The resources in question are a set of cameras which overlook a common area from varying viewing angles. These cameras are heterogenous and have different parameters for control, e.g. some are static, some are pan, tilt and zoom cameras. Information theoretic measures are used to choose the best surveillance parameters for these cameras, whereas best can be defined by higher level reasoning, or human operators. Currently, the work concentrates on objective functions from information-theory and the use of sensor data fusion techniques to make informed decisions.

    作为爱马仕项目的一部分，目标是建立一个感知/行动周期，并以不同的变焦水平进行特定考虑。分布式摄像头系统可以解释为一种抽象传感器，其内容具有更高级别的目标作为输入。

    代理表示的最粗糙的比例被认为是跟踪试剂并注意其轨迹以及其他粗略的特征，这些特征将对动作和意图识别有用。然后，目的是生成有关代理本身的行为和概念描述及其与现场其他代理和预定义对象有关的关系。

    Cognitive Computer Vision
    Recent work in visual tracking and camera control has looked at the issues involved in activity recognition using parametric and non-parametric belief propagation in Bayesian Networks, and begun to touch on the issues of causality. The current research takes all of these areas forward. The ultimate goal will be to combine these techniques to produce a pan/tilt/zoom camera system, and/or network of cameras, that can allocate attention in an intelligent fashion via an understanding of the scene, inferred automatically from visual data.

    该主题与欧盟项目爱马仕（Hermes）直接相关，这是智能视觉监视的令人兴奋且与社会相关的领域。该研究的目的是开发可以被认为可以通过开发算法和本体来理解视觉场景的算法和本体，这些相机系统可以被认为可以表现出紧急的认知行为。

  • Cognitive Computer Vision

  • Recent work in visual tracking and camera control has looked at the issues involved in activity recognition using parametric and non-parametric belief propagation in Bayesian Networks, and begun to touch on the issues of causality. The current research takes all of these areas forward. The ultimate goal will be to combine these techniques to produce a pan/tilt/zoom camera system, and/or network of cameras, that can allocate attention in an intelligent fashion via an understanding of the scene, inferred automatically from visual data.

    该主题与欧盟项目爱马仕（Hermes）直接相关，这是智能视觉监视的令人兴奋且与社会相关的领域。该研究的目的是开发可以被认为可以通过开发算法和本体来理解视觉场景的算法和本体，这些相机系统可以被认为可以表现出紧急的认知行为。

    在这种情况下，最初研究了基于模糊的时间逻辑的解决方案，将模糊推断与动作连接起来，以实时控制PAN/TILT/ZOOM摄像机。算法应在相机节点网络上进行测试，每个算法都提供了用于本地处理和对执行器的低级控制的计算机单元。

    该项目的另一个重要方面是调查认知愿景的新解决方案，重点是智能监视设备。更具体地说，要对视频进行因果推理进行研究，并将其与活动识别，视觉跟踪算法以及对PAN/TILT/ZOOM设备的积极控制以及其他适用于创建智能视觉监视设备的广泛问题的工作。

  • 头部姿势的行为

  • 我们已经开发了算法来通过新颖的随机蕨类植物分类器来估计头部姿势。分类器不是直接测量图像的头部姿势，而是根据头部姿势将图像分组为组。为了使头部姿势估计器在现实情况下有效，必须能够应对不同的皮肤和头发颜色，以及在照明方向，强度和颜色方面的广泛变化。大多数现有的分类器都容易受到这些变化的影响，并且需要具有不同组合的照明条件和皮肤/头发颜色变化的示例，以进行准确的分类。我们采用的方法有效地学习了每个观察到的新人的皮肤和头发颜色的模型，这使视频中人们在很大程度上不变和人的个人特征。结果是一个分类器，可在非常低的分辨率视频中起作用，该视频的直径仅为10像素。

  • 通过跟踪移动对象来避免在视觉猛击中移动离群值

  • 要以视频速率工作，单眼猛击构建的地图必然会很稀疏，这使它们对移动点的错误包含以及通过临时闭塞删除有效点的删除敏感。该系统提供了与3D对象跟踪器的单眼（单眼同时定位和映射）的并行实现，从而允许有关移动对象和遮挡的推理。SLAM进程为对象跟踪器提供了将对象注册到地图框架中的信息，并且对象跟踪器允许标记功能，即移动对象上的那些移动功能，或者是由其阻塞边缘创建的那些伪用者或那些由封闭的边缘创建的伪用力，或对象。虽然传统的单峰假设存在僵化的环境，但有时会降低性能，有时甚至在包括运动功能时，组合系统对动态环境更为强大。此外，知道某些静态特征被阻塞而不是不可靠的知识避免了需要引起某种特征删除的过程，随后可能会进行不必要的重新定位，从而延长了闭塞静态特征的寿命。

    The object tracker is done using a modified version of Harris' RAPiD tracker. The identification and pose initialization are at present done by hand. The videos are presented to verify the recovered geometry and to indicate the impact on camera pose in monoSLAM of including and avoiding moving features. The system without the object tracker gives the incorrect camera's pose due to moving features, but still survives until the end of the video. The system with the object tracker, on the other hand, estimates more correct camera's pose through the image sequences.

  • Perseus：跟踪计算机交互的手

  • 该项目的目的是通过实时解释手动和手势来提供一种自然而直观的方式与计算机交互。以非侵入性方式获取此功能的一种经济有效的方法是使用相机的视觉传感。

    The core of this project is an algorithm that integrates segmentation, 3D pose estimation of a human hand by use of a simplified 3D hand model and a mapping of the pose parameters into a latent space. In order to be able to track in 3D a non rigid articulated object (like a human hand) it must first be able to track in 3D rigid non articulated objects.

    我们一直在研究的算法涉及在Charles Bibby和Ian Reid在主动视觉组内开发的2D刚性对象跟踪的跟踪算法中添加3D形状信息，并在其论文中使用Pixel-Wise wise tosteriors进行了强大的实时视觉跟踪。该算法将图像视为一袋像素（图像中像素的位置被认为是随机变量），然后通过使用像素的后倍数而不是可能性而不是可能性来进化一个级别集合函数。这种方法在标准硬件上实时工作。我们正在努力添加一个新的先验：具有正确调整的姿势参数的3D对象模型的渲染之间的差异和图像的分段区域（由级别设置函数定义）。然后，该区域将进化为3D对象的投影。

    尽管在对刚性对象的姿势参数进行优化的情况下，上面介绍的算法应起作用，但对于获得非刚性，铰接式，物体的姿势而言，可能太慢了。为此，我们正在考虑使用高维姿势空间和低维潜在空间之间的高斯过程潜在变量模型映射。

    The system uses a custom 3D engine. Traditional 3D rendering engines (like OpenGL or DirectX) lose the relation been 3D points before a transform (rotation, translation and projection) and their resulting 2D projections, during the rendering process. Out engine is able to keep this relation and render a 3D object in wireframe, filled and outline only mode, apply a Scharr filter and compute the distance transform in only a couple of milliseconds. This level of performance is achieved using parallel algorithms developed for the NVIDIA CUDA framework.

  • 同时识别和本地化场景增强

  • A system has been developed which combines single-camera SLAM (Simultaneous Localization and Mapping) with established methods for feature recognition. Besides using standard salient image features to build an on-line map of the camera's environment, it is capable of identifying and localizing known planar objects in the scene, and incorporating their geometry into the world map. Continued measurement of these mapped objects improves both the accuracy of estimated maps and the robustness of the tracking system. In the context of hand-held or wearable vision, the system's ability to enhance generated maps with known objects increases the map's value to human operators, and also enables meaningful automatic annotation of the user's surroundings. The presented solution lies between the high order enriching of maps such as scene classification, and the efforts to introduce higher geometric primitives such as lines into probabilistic maps. The object detection is done using SIFT. A database of known objects are compared to scene images and when a match is found the 3D location of the object is calculated using a homography and placed in the SLAM map with a high level of accuracy.

    Thevideo比较在唾液屏幕视图中运行和没有对象检测的单眼大满贯系统。由于功能不足，没有对象检测的系统会松开轨道，此时，视频放缓以突出显示。具有对象检测的系统继续进行，在视频的末尾，它成功地检测到了所有五个对象，并将其准确地定位在世界范围内。

  • Relocalization of a lost camera in SLAM