基于rMC和RPN的特定目标动作识别网络
An Action Recognition network for specific target based on rMC and RPN
摘要：
传统的动作识别方法对操作者没有针对性，当视频中有其他动作时，识别结果容易受到干扰。本文提出了一种基于混合卷积resnet和RPN的网络。在UCF-101数据集上对rMC进行了测试，并与R3 D方法进行了比较。结果表明，该方法的正确率达到71.07%.同时，在我们的手势和身体姿势数据集的特定目标的动作识别网络进行了测试。仿真结果表明，该系统运行速度达到了200 FPS。最后，通过引入回归块对模型进行了改进，并取得了较好的效果，显示了该模型的巨大潜力。
英文摘要：
The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
