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

该项目更广泛的影响/商业潜力源于解决VR/AR（预计到2020年将增长到1500亿美元），机器人和物联网（到2019年将分别达到1350亿美元和1.7万亿美元）的重要手势输入挑战。如果这项技术成功地降低了高技术风险，它将代表着手势识别3D手部模型技术的巨大飞跃，并有可能成为AR、VR、机器人和物联网应用的行业标准，对教育、医疗和医疗保健产生广泛的社会影响。其更广泛的影响进一步放大的潜力，在服务的需要，残疾人社区，提高他们的生活质量，通过更好地能够沟通，学习和适应他们的互动需求。这个小企业技术转让（STTR）第二阶段项目旨在显着推进当前的三维手势识别技术，通过开发一个动态的手跟踪模型的手势意图识别。该算法对遮挡具有鲁棒性，并能容忍摄像机方向和位置的变化.这项研究将使目前的学术和商业手部模型产生变革性的飞跃，并克服迄今为止被证明难以克服的关键技术障碍。它解决了以下关键挑战，并涉及非常高的技术风险：1）在持有物体时的鲁棒手部跟踪和2）使用物体而不使用任何基准标记的鲁棒有形交互2）用于触摸交互检测的低轮廓手持可穿戴设备。第2阶段项目将通过以下方式实现这些目标：1）数据采集和手-物体姿态估计; 2）通过增强的有形交互理解用户意图; 3）系统验证和用户测试。

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.