RI: Small: Computational Models of Context-awareness and Selective Attention for Persistent Visual Target Tracking

RI：小型：持续视觉目标跟踪的上下文感知和选择性注意的计算模型

• 批准号: 0916607
• 负责人: Ying Wu
• 金额: $ 37.6万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916607&HistoricalAwards=false
• 关键词: RI; Small; Computational; Models; Context

Although persistent and long-duration tracking of general targets is a basic function in the human vision system, this task is quite challenging for computer vision algorithms, because the visual appearances of real world targets vary greatly and the environments are heavily cluttered and distractive. This large gap has been a bottleneck in many video analysis applications. This project aims to bridge this gap and to overcome the challenges that confront the design of long-duration tracking systems, by developing new computational models to integrate and represent some important aspects in the human visual perception of dynamics, including selective attention and context-awareness that have been largely ignored in existing computer vision algorithms. This project performs in-depth investigations of a new computational paradigm, called the synergetic selective attention model that integrates four processes: the early selection process that extracts informative attentional regions (ARs), the synergetic tracking process that estimates the target motion based on these ARs, the robust integration process that resolves the inconsistency among the motion estimates of these ARs for robust information fusion, and the context-aware learning process that performs late selection and learning on-the-fly to discover contextual associations and to learn discriminative-ARs for adaptation. This research enriches the study of visual motion analysis by accommodating aspects from the human visual perception and leads to significant improvements for video analysis. It benefits many important areas including intelligent video surveillance, human-computer interaction and video information management. The project is linked to educational activities to promote learning and innovation through curriculum development, research opportunities, knowledge dissemination through conferences and the internet as well as other outreach activities, and the involvements of underrepresented groups.

虽然对一般目标的持续长时间跟踪是人类视觉系统中的一项基本功能，但这项任务对计算机视觉算法来说是相当具有挑战性的，因为现实世界中目标的视觉外观变化很大，环境严重混乱和分散注意力。这一巨大的差距已经成为许多视频分析应用的瓶颈。该项目旨在弥合这一差距，并克服长期跟踪系统设计所面临的挑战，方法是开发新的计算模型，以整合和表示人类视觉感知动态中的一些重要方面，包括选择性注意和上下文感知，这些在现有计算机视觉算法中基本上被忽视。该项目深入研究了一种新的计算范式，称为协同选择性注意模型，它集成了四个过程：提取信息注意区域(AR)的早期选择过程，基于这些AR估计目标运动的协同跟踪过程，用于解决这些AR的运动估计之间的不一致性的稳健整合过程，以及执行延迟选择和动态学习以发现上下文关联并学习用于适应的区分AR的上下文感知学习过程。该研究从人类视觉感知的角度丰富了视觉运动分析的研究，并对视频分析有了显着的改进。它使智能视频监控、人机交互和视频信息管理等许多重要领域受益匪浅。该项目与通过课程开发、研究机会、通过会议和互联网传播知识以及其他外联活动以及代表人数不足的群体的参与来促进学习和创新的教育活动相联系。