RI: Small: Global, Stable Descriptors of Visual Motion

RI：小：全局、稳定的视觉运动描述符

• 批准号: 1420894
• 负责人: Carlo Tomasi
• 金额: $ 45万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1420894&HistoricalAwards=false
• 关键词: RI; Small; Global; Stable; Descriptors

This project studies the fundamental mathematics of how to describe the motions visible in a video recording. The developed techniques allow accurately delineating the boundaries between image regions that move differently from each other. The resulting description of motion can be used as input for recognizing activities in video. Applications include surveillance, traffic monitoring, video retrieval, robot navigation, assistance to human vehicle drivers, medical diagnosis of movement pathology, assessment of performance in sports or other activities, sign language recognition, and automatic video annotation.Current approaches define visual motion as a point-to-point mapping across video frames. However, image data in poorly textured areas constrain point motion weakly if at all. Since these areas are pervasive, computing point-to-point motion requires strong and often arbitrary assumptions about the scene. This project redefines image motion as a curve-to-curve mapping. The curves in question are iso-contours, that is, the curves in each video frame along which image brightness is constant. Techniques from computational topology are used and extended to describe how iso-contours in one frame connect to those in the next, forming surfaces in spacetime. The concept of persistence from computational topology, together with a new notion of feature longevity, allow separating ephemeral changes caused by image noise or lighting artifacts from features that reoccur consistently over time. The research can provide a global, topological, stable description of image motion. The research team evaluates the techniques on both existing video and on sequences newly recorded with specialized cameras to isolate different technical challenges in turn. Other researchers can use these sequences for further experimentation when they are ready to be published.

该项目研究如何描述视频记录中可见的运动的基础数学。所开发的技术允许准确地描绘移动彼此不同的图像区域之间的边界。所得到的运动描述可以用作用于识别视频中的活动的输入。应用包括监视、交通监控、视频检索、机器人导航、辅助人类车辆驾驶员、运动病理的医学诊断、运动或其他活动中的表现评估、手语识别和自动视频注释。然而，纹理不良区域中的图像数据对点运动的约束很弱。由于这些区域是普遍存在的，计算点到点的运动需要对场景进行强有力的、通常是任意的假设。这个项目将图像运动重新定义为曲线到曲线的映射。所讨论的曲线是等轮廓线，即，每个视频帧中的曲线沿着其图像亮度是恒定的。从计算拓扑学的技术被用来和扩展，以描述如何在一个框架中的等值线连接到那些在下一个，在时空中形成表面。从计算拓扑的持久性的概念，加上一个新的概念的功能寿命，允许分离的短暂变化所造成的图像噪声或照明文物的功能，随着时间的推移不断重复。该研究可以提供一种全局的、拓扑的、稳定的图像运动描述。研究团队评估了现有视频和用专用摄像机新录制的序列的技术，以依次隔离不同的技术挑战。其他研究人员可以在准备发表时使用这些序列进行进一步的实验。