Collaborative: Gesture Recognition Challenge

协作：手势识别挑战

• 批准号: 1128296
• 负责人: Vassilis Athitsos
• 金额: $ 4.99万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1128296&HistoricalAwards=false
• 关键词: Collaborative; Gesture; Recognition; Challenge

The objective of this research is both to advance the field of video data processing (more particularly gesture recognition) and to illustrate the power of deep learning architectures and transfer learning. The approach is to organize a challenge culminating in a life evaluation at the site of a conference.Intellectual merit: Much of the recent research in Adaptive and Intelligent Systems (AIS) has sacrificed the grand goal of designing systems ever approaching human intelligence for solving data mining tasks of practical interest with more immediate reward. This project gives an opportunity to deep learning architectures inspired by neural networks to demonstrate their ability to address more complex problems requiring to transfer knowledge from task to task (transfer learning), leveraging the availability of video data not directly related to the target task of gesture recognition. The participants will also be involved in a data exchange to grow an unprecedented large and diverse database of gestures. Broader Impact: Challenges have proved to be a great stimulus of research. For a long lasting impact, the challenge platform and the data and software repositories will remain open beyond the term of the NSF funded project. The educational components of the project include engaging students in the contest, providing material directly usable in teaching curricula, and demonstrating gesture recognition to high school students to expose them to computer vision research and sign language communication. Our connections with the deaf community will allow us to gear the product of this research to advance assistive technology.

这项研究的目的既是提高视频数据处理（尤其是手势识别）的领域，并说明深度学习体系结构的力量和转移学习的力量。这种方法是在会议场所的生活评估中组织一项挑战。智能优点：自适应和智能系统（AIS）最近的许多研究牺牲了设计系统的宏伟目标，即设计人类情报以解决实践兴趣的数据挖掘任务，并获得更多的立即奖励。该项目为受神经网络启发的深度学习体系结构提供了机会，以证明其能够解决需要将知识从任务转移到任务（转移学习）的更复杂问题的能力，从而利用了与手势识别目标任务不直接相关的视频数据的可用性。参与者还将参与数据交换，以增强前所未有的大型和多样化的手势数据库。更广泛的影响：事实证明，挑战是研究的巨大刺激。为了产生持久的影响，挑战平台以及数据和软件存储库将在NSF资助的项目的任期内保持开放。该项目的教育组成部分包括吸引学生参加比赛，提供直接在教学课程中使用的材料，并向高中生展示识别姿态，使他们接触到计算机视觉研究和手语语言交流。我们与聋人社区的联系将使我们能够为这项研究的产品做好准备，以推进辅助技术。