DA-GCN-based coal mine personnel action recognition method
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摘要: 针对煤矿生产区域的监控视频较为模糊且人员行为类型复杂，常规行为识别方法的准确率较低的问题，提出了一种基于动态注意力与多层感知图卷积网络（DA-GCN）的煤矿人员行为识别方法。采用Openpose算法提取输入视频的人体关键点，得到3个维度、18个坐标的人体关键点信息，降低模糊背景信息的干扰；通过动态多层感知图卷积网络（D-GCN）提取人体关键点的空间特征，通过时间卷积网络（TCN）提取人体关键点的时间特征，提高网络对不同动作的泛化能力；使用动态注意力机制，增强网络对于动作关键帧、关键骨架的注意力程度，进一步缓解视频质量不佳带来的影响；使用Softmax分类器进行动作分类。通过场景分析，将井下行为分为站立、行走、坐、跨越和操作设备5种类型，构建适用于煤矿场景的Cumt-Action数据集。实验结果表明，DA-GCN在Cumt-Action数据集的最高准确率达到99.3%，最高召回率达到98.6%；与其他算法相比，DA-GCN在Cumt-Action数据集和公共数据集NTU-RGBD上均具有较高的识别准确率，证明了DA-GCN优秀的行为识别能力。Abstract: At present, the monitoring video in coal mine production area is vague, the type of personnel actions is complex, and the accuracy of conventional action recognition methods is low. In order to solve the above problems, a coal mine personnel action recognition method based on dynamic attention and multi-layer perception graph convolutional network (DA-GCN) is proposed. The Openpose algorithm is used to extract the key points of the human body in the input video to obtain the key point information of the human body in 3 dimensions and 18 coordinates, reducing the interference of fuzzy background information. The spatial characteristics of the key points of the human body is extracted by dynamic multilayer perception graph convolution network (D-GCN), and the temporal characteristics of the key points of the human body is extracted by temporal convolutional network (TCN) so as to improve the generalization ability of the network for different actions. The dynamic attention mechanism is used to enhance the network's attention to action key frames and key skeletons to further mitigate the impact of poor video quality. The softmax classifier is used for action classification. Through scene analysis, underground actions are classified into five types, including standing, walking, sitting, crossing and operating equipment. The method constructs a Cumt-Action data set that applicable to coal mine scenes. The experimental results show that the highest accuracy rate of D-GCN in the Cumt-Action data set is 99.3%, and the highest recall rate is 98.6%. Compared with other algorithms, DA-GCN has higher recognition accuracy in both the Cumt-Action data set and the public data set NTU-RGBD.
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