Action recognition algorithm based on skeletal joint data and adaptive time pyramid
Action recognition algorithm based on skeletal joint data and adaptive time pyramid
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DOI：
10.1007/s11760-021-02116-9
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Mingjun Sima;Mingzheng Hou;Xin Zhang;Jian Ding;Ziliang Feng
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文献类型：
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作者：
Mingjun Sima;Mingzheng Hou;Xin Zhang;Jian Ding;Ziliang Feng
人体动作识别技术在视频监控、视频检索、运动医学、人机交互等领域发挥着至关重要的作用。这项技术的研究和应用进展缓慢，局限于复杂的环境和人类行为的可塑性。Kinect作为一种新型传感器，为人体动作识别提供了一种新的思路，它可以同步获取目标的骨骼关节点数据。本文提出了一种基于骨骼关节数据的人体动作识别方法。首先将人体动作的运动信息和静态信息融合为特征，然后利用骨骼向量构建特征提取后能够描述人体动作变化的运动模型。然后将模型引入到自适应时间金字塔中，获取全局和局部信息；进一步对各时间段的骨关节特征进行了处理。最后，将核极限学习机用于人体动作识别。实验结果表明，与其他方法相比，我们的工作成功地获得了骨架信息。
Human action recognition technology plays an crucial role in the fields of video surveillance, video retrieval, sports medicine and human–computer interaction. Slow research and application of this technology limited to complex environments and plasticity of human action. As a new sensor, Kinect provides a new idea for human action recognition, which can synchronously obtain data of skeleton joint points from target. In this paper, we propose a human action recognition method using skeletal joints data. The motion and static information of human action are firstly fused as feature and skeletal vector is used to construct motion model which can describe variation of human action after feature extraction. Then the model is introduced into adaptive time pyramid to capture global and local information; furthermore, skeletal joints feature in each period of time is processed. Finally, kernel extreme learning machine is used for human action recognition. Experimental results show that our work successfully achieves skeleton information in comparison with other methods.