结合Attention-ConvLSTM的双流卷积行为识别

针对传统方法在通过视频数据进行人体行为识别的过程中,无法准确分析长时间范围的运动信息,不能很好地利用运动信息中的局部特征和其空间关系.提出将基于注意力机制的卷积长短时记忆神经网络(Attention-ConvLSTM)与传统的双流卷积进行结合,实现了对视频数据中运动信息的非线性特征更好的学习,对局部显著特征及其空间关系...

Two Stream CNN with Attention-ConvLSTM on Human Behavior Recognition

In the process of human behavior recognition based on video data,traditional methods can't accurately analyze the motion information in a long time range,and can't make good use of the local features and their spatial relations in the motion information.In this paper,the convolution long-term memory neural network(Attention-ConvLSTM)based on attention mechanism is combined with the traditional two stream convolution to realize better learning of the non-linear features of motion information in video data,and to make better use of the local salient features and their spatial relations.This paper also designs a new regularized cross-entropy loss function,which makes the extended neural network achieve faster convergence.Compared with the traditional methods,the performance of our method in UCF101 and HMDB51 is significantly improved.