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引用次数: 32
摘要
动作识别在包括智能家居和个人辅助机器人在内的各种应用中发挥着重要作用。在本文中,我们提出了一种利用动作捕捉动作数据来识别人类动作的算法。动作捕捉数据提供了构成人体骨骼的关节的精确三维位置。为了对动作进行分类,我们在时间上对骨骼关节的运动进行建模。动作序列每一帧中的骨架表示为一个129维向量,其中每个分量是由每个关节在骨架上的固定点构成的一个三维角度。最后,将视频表示为从所有动作序列获得的码本上的直方图。与此同时,骨骼关节的时间方差被用作附加特征。使用元认知径向基函数网络(McRBFN)及其基于投影的学习(PBL)算法对动作进行分类。我们在广泛使用的Berkeley Multimodal Human Action Database (MHAD)上实现了超过97%的识别准确率。
Action recognition from motion capture data using Meta-Cognitive RBF Network classifier
Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 3D angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).