基于自学习特征与HMM的人体动作识别(英文)

Human Action Recognition Based on Self-Learning Feature and HMM

利用机器视觉进行人体动作识别的方法大多数基于手工特征并需要先验知识,这类方法不可避免地依赖于特定问题而忽略了视觉信息的内在结构。提出了一种利用自学习特征及姿态组合规则进行有效动作识别的新方法。使用稀疏自编码(SAE)网络提取轮廓图像的结构特征并构造姿态码本。在识别阶段,使用隐马可夫模型(HMM)训练不同动作类别的模型。设计了一种关键帧提取算法用于在训练HMM前降低长序列的冗余度。通过仿真实验验证了该方法的有效性。

The current methods of human action recognition by computer vision are mostly based on hand-craft features and usually prior knowledge-required. They inevitably depend on specific applications and neglect the inner structure of visional information. A novel method which integrated self-learned pose features and combined posture symbol rules was proposed to achieve the recognition of human action more efficiently. The structural features of posture silhouette were extracted and a codebook of primary posture was built through the establishment of a sparse auto-encoder network. Then, in the phase of recognition, the Hidden Markov Model was employed to train the models for different action categories. Besides, a key frame extraction algorithm was developed to reduce the redundancy of long code sequence before training HMMs. Simulation experiments manifest the effectiveness of the proposed method.