基于深度学习的自动扶梯视频人体动作识别

Human Motion Recognition in Escalator Video based on Deep Learning

在自动扶梯场景下的视频人体动作识别中,视频数据源不稳定,如遮挡、多视角、光照、低分辨率、动态背景以及背景混乱等均导致动作分类及检测不准确。针对这些问题,提出使用基于改进的SlowFast网络的人体动作识别方法,以更好地捕获视频连续帧中隐藏的时间和空间信息。通过与R(2+1)D卷积网络模型的识别准确率进行对比,改进的SlowFast网络模型在视频中的动作分类和检测方面都表现了很好的性能,能够有效地解决自动扶梯场景下的人体动作识别问题。

In human motion recognition in escalator scene video,the instability of the video data source,such as occlusion,multiple viewing angles,illumination,low resolution,dynamic background,and background confusion,leads to inaccurate motion classification and detection.Aiming at these problems,this paper proposes to use a human motion recognition method based on the improved SlowFast network to better capture the temporal and spatial information hidden in the continuous video frames.Compared with the recognition accuracy of the R(2+1)D convolutional network model,the improved SlowFast network model has achieved better performance in motion classification and detection in videos,and can effectively solve the problem of Human body motion recognition in escalator scene.