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.

尽管对一般目标的持久和长期跟踪是人类视觉系统的基本功能，但对于计算机视觉算法而言，这项任务非常具有挑战性，因为现实世界目标的视觉外观差异很大，而且环境越来越杂乱无章。在许多视频分析应用程序中，这个较大的差距一直是瓶颈。该项目旨在通过开发新的计算模型来整合并代表人类对动态的视觉感知的一些重要方面，包括在现有的计算机视觉算法中忽略了人们在人体视觉上的视觉感知，包括在人类对动态的视觉感知中综合和代表一些重要方面，以克服这一差距并克服面临长期跟踪系统设计的挑战。 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选择和学习，以发现上下文关联并学习适应性的歧视性。 这项研究通过适应人类视觉感知的各个方面来丰富视觉运动分析的研究，并导致视频分析的重大改进。它有益于许多重要领域，包括智能视频监视，人为计算机互动和视频信息管理。 该项目与教育活动有关，旨在通过课程发展，研究机会，通过会议和互联网以及其他外展活动以及代表性不足的群体的参与来促进学习和创新。