STTR Phase II: Dynamic Robust Hand Model for Gesture Intent Recognition

STTR 第二阶段：用于手势意图识别的动态鲁棒手部模型

• 批准号: 1738888
• 负责人: Raja Jasti
• 金额: $ 75万
• 依托单位: ZeroUI Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738888&HistoricalAwards=false
• 关键词: STTR; Phase; II; Dynamic; Robust

The broader impact/commercial potential of this project stems from addressing theimportant hand gesture based input challenges of VR/AR (expected to grow to $150B by2020), Robotics and IoT ($135B by 2019 and $1.7T by 2020 respectively). This technology,if successful in mitigating the high technical risks, represents a huge leap in the state of theart in 3D hand models for gesture recognition and has the potential to be the industrystandard for AR, VR, Robotics and IoT applications with broad societal impact in education,medical and healthcare. Its broader impact is further amplified by the potential in serving theneeds of the disabled community in improving their quality of life by being better able tocommunicate, learn and adapt to their interaction needs.This Small Business Technology Transfer (STTR) Phase 2 project aims to significantlyadvance current 3D hand gesture recognition technology by developing a dynamic handtracking model for gesture intent recognition. It is robust against occlusion and tolerant tovariations in camera orientation and position. This research will result in a transformativeleap above the current state of academic and commercial hand models and overcome keytechnical hurdles that have so far proven difficult to overcome. It solves the following keychallenges and involves very high technical risks: 1) robust hand tracking while holdingobjects and 2) robust tangible interactions using objects without using any fiducial markers2) low profile hand wearable for touch interaction detection. This Phase 2 project willachieve these objectives by 1) data acquisition and hand-object pose estimation, 2)understanding user intents with enhanced tangible interactions, and 3) system validationand user testing.

该项目的更广泛的影响/商业潜力源于解决重要的基于手势的VR/AR的投入挑战（预计将增长到$ 150B BY 2020），机器人和物联网（到2019年为135B美元，分别为2020年，分别为1.7 t）。如果成功地减轻高技术风险，这项技术代表了3D手模型中的Theart状态的巨大飞跃，并有可能成为AR，VR，机器人和物联网应用的行业标准，并在教育，医疗和医疗保健中产生广泛的社会影响。通过更好地为残疾人社区服务的潜力，通过更好地提高他们的互动需求来改善其生活质量的潜力进一步扩大了其更广泛的影响。这项小型企业技术转移（STTR）第2阶段项目旨在通过开发动态手段的模型来实现GESTENTICTICTICTICTIONT GOTENTICTICTIONT。在摄像机方向和位置中的遮挡和耐受性易变是可靠的。这项研究将导致超过学术和商业手部模型的现状，并克服迄今为止难以克服的键盘障碍。它解决了以下键challenges，并涉及非常高的技术风险：1）稳健的手动跟踪以及2）使用对象无需使用任何基金标记的对象的稳健有形互动2）低调手工可戴的触摸互动检测。该阶段2项目将通过1）数据采集和手动姿势估计，2）了解具有增强的切实交互的用户意图，以及3）系统验证和用户测试。