公开(公告)号：WO2016046212A1

公开(公告)日：2016-03-31

申请号：PCT/EP2015/071756

申请日：2015-09-22

Applicant: FOUNDATION FOR RESEARCH AND TECHNOLOGY - HELLAS (FORTH)

Inventor： MICHEL, Damien ,  ARGYROS, Antonis

IPC: G06K9/00

CPC classification number: G06K9/00362 ,  G06K9/00335 ,  G06K2209/055 ,  G06T2200/04 ,  G06T2207/10021 ,  G06T2207/10024 ,  G06T2207/10028

Abstract: The ARS offers tracking, estimation of position, orientation and full articulation of the human body from marker-less visual observations obtained by a camera, for example an RGBD camera. An ARS may provide hypotheses of the 3 D configuration of body parts or the entire body from a single depth frame. The ARS may also propagates estimations of the 3 D configuration of body parts and the body by mapping or comparing data from the previous frame and the current frame. The ARS may further compare the estimations and the hypotheses to provide a solution for the current frame. An ARS may select, merge, refine, and/or otherwise combine data from the estimations and the hypotheses to provide a final estimation corresponding to the 3 D skeletal data and may apply the final estimation data to capture parameters associated with a moving or still body.

Abstract translation: ARS通过相机（例如RGBD相机）获得的无标记的视觉观察，提供跟踪，估计位置，方位和人体完整的关节。 ARS可以从单个深度框架提供身体部位或整个身体的3D配置的假设。 ARS还可以通过映射或比较来自前一帧和当前帧的数据来传播身体部位和身体的3D配置的估计。 ARS可以进一步比较估计和假设以为当前帧提供解决方案。 ARS可以选择，合并，细化和/或以其他方式组合来自估计和假设的数据以提供对应于3D骨架数据的最终估计，并且可以应用最终估计数据来捕获与移动或静止体相关联的参数。

公开(公告)号：WO2016042039A1

公开(公告)日：2016-03-24

申请号：PCT/EP2015/071250

申请日：2015-09-16

Applicant: FOUNDATION FOR RESEARCH AND TECHNOLOGY - HELLAS (FORTH)

Inventor： MICHEL, Damien ,  PAPOUTSAKIS, Konstantinos ,  ARGYROS, Antonis

IPC: G06K9/00 ,  G06K9/44 ,  G06K9/46

CPC classification number: G06K9/00355 ,  G06F3/011 ,  G06F3/017 ,  G06K9/00389 ,  G06K9/44 ,  G06K9/4609 ,  G06K9/469 ,  G06K9/52 ,  G06K9/6202 ,  G06T7/13 ,  G06T7/246 ,  G06T7/50 ,  G06T7/60 ,  G06T7/73 ,  G06T2207/30196

Abstract: The Gesture Recognition Apparatuses, Methods And Systems For Human-machine Interaction ("GRA") discloses vision-based gesture recognition. GRA can be implemented in any application involving tracking, detection and/or recognition of gestures or motion in general. Disclosed methods and systems consider a gestural vocabulary of a predefined number of user specified static and/or dynamic hand gestures that are mapped with a database to convey messages. In one implementation, the disclosed systems and methods support gesture recognition by detecting and tracking body parts, such as arms, hands and fingers, and by performing spatio-temporal segmentation and recognition of the set of predefined gestures, based on data acquired by an RGBD sensor. In one implementation, a model of the hand is employed to detect hand and finger candidates. At a higher level, hand posture models are defined and serve as building blocks to recognize gestures based on the temporal evolution of the detected postures.

Abstract translation: 用于人机交互的手势识别装置，方法和系统（“GRA”）公开了基于视觉的手势识别。 GRA可以在涉及手势或运动的跟踪，检测和/或识别的任何应用中实现。 公开的方法和系统考虑与数据库映射以传达消息的预定义数量的用户指定的静态和/或动态手势的手势词汇表。 在一个实现中，所公开的系统和方法通过检测和跟踪身体部位（例如手臂，手和手指）以及通过基于由RGBD获取的数据执行预定义姿态集合的时空分割和识别来支持手势识别 传感器。 在一个实现中，使用手的模型来检测手和手指候选。 在更高层次上，手势模型被定义并且作为基于检测到的姿势的时间演变来识别手势的构建块。