Collaborative: Gesture Recognition Challenge

协作：手势识别挑战

• 批准号: 1128436
• 负责人: Isabelle Guyon
• 金额: $ 4.98万
• 依托单位: Clopinet
• 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=1128436&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.

这项研究的目的是推进视频数据处理领域（特别是手势识别），并说明深度学习架构和迁移学习的力量。这种方法是组织一个挑战，最终在现场的一个会议的生命评估。智力的优点：最近的研究在自适应和智能系统（AIS）牺牲了设计系统的宏伟目标，不断接近人类的智能解决数据挖掘任务的实际利益与更直接的回报。该项目为受神经网络启发的深度学习架构提供了一个机会，以展示它们解决更复杂问题的能力，这些问题需要将知识从一个任务转移到另一个任务（转移学习），利用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，以建立一个前所未有的庞大而多样化的手势数据库。更广泛的影响：挑战已被证明是研究的巨大刺激。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资助项目的期限之后保持开放。该项目的教育部分包括让学生参与竞赛，提供可直接用于教学课程的材料，并向高中生演示手势识别，使他们接触计算机视觉研究和手语交流。我们与聋人社区的联系将使我们能够将这项研究的产品用于推进辅助技术。