复杂场景下基于R-FCN的手势识别
发布时间：2018-10-08 16:48
【摘要】：为了解决复杂场景下手势识别的问题,将基于区域的全卷积网络(R-FCN)用于手势识别.为了使网络适应复杂场景,利用在线难例挖掘技术对手势识别过程中产生的难例进行在线学习,并结合手的特征对网络参数进行优化调节.实验结果表明:基于R-FCN的手势识别方法能准确地从复杂场景中识别手势,识别率达到99.73%.
[Abstract]:In order to solve the problem of hand gesture recognition in complex scenes, a region based full convolution network (R-FCN) is applied to gesture recognition. In order to adapt the network to the complex scene, the online hard case mining technique is used to study the difficult cases in the process of hand gesture recognition, and the network parameters are optimized and adjusted with the hand features. The experimental results show that the gesture recognition method based on R-FCN can recognize gestures accurately from complex scenes, and the recognition rate is 99.73.
【作者单位】： 华中科技大学自动化学院;华中科技大学图像信息处理与智能控制教育部重点实验室;
【分类号】：TP391.41
[Abstract]:In order to solve the problem of hand gesture recognition in complex scenes, a region based full convolution network (R-FCN) is applied to gesture recognition. In order to adapt the network to the complex scene, the online hard case mining technique is used to study the difficult cases in the process of hand gesture recognition, and the network parameters are optimized and adjusted with the hand features. The experimental results show that the gesture recognition method based on R-FCN can recognize gestures accurately from complex scenes, and the recognition rate is 99.73.
【作者单位】： 华中科技大学自动化学院;华中科技大学图像信息处理与智能控制教育部重点实验室;
【分类号】：TP391.41
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