基于注意力机制的群组行为识别方法

Group Activity Recognition Method Based on Attention Mechanism

在基于视频图像的群组行为识别方法中,传统的深度学习方法大多使用标准(最大/平均)池化操作对卷积特征进行处理,并且未考虑群组行为中的关键人物对群组行为分类的重要性。针对以上问题,本文提出一种基于注意力机制的模型来检测群组行为视频中的行为,重点关注活动中的关键人物,根据注意力权重的不同分配动态地对卷积特征进行池化,最终正确识别视频图像中的群组行为。此模型在群组行为数据集CAD(Collective activity dataset)和CAE(Collective activity extendeddataset)上的识别准确率优于许多使用标准池化结构的现有模型。

In the video image based group activity recognition method,the traditional deep learning methods generally use the conventional(maximum/average)pooling to process the convolutional feature.However,these methods do not consider the importance of the key characters in the group activity which influence the classified result of group behavior.Therefore,we propose an attention based model to detect behavior in group activity videos.In order to identify the group behavior correctly in the video image,this model focuses on the key people in the activity and pools convolutional features dynamically according to the weight of the attention.We conduct extensive experiments on two group behavior datasets,CAD(Collective activity dataset)and CAE(Collective activity extended dataset).The recognition accuracy of our model is better than many existing models using conventional pooling structure.