基于深度卷积神经网络和深度视频的人体行为识别

传统人体行为识别基于人工设计特征方法涉及的环节多,具有时间开销大,算法难以整体调优的缺点。以深度视频为研究对象,构建了3维卷积深度神经网络自动学习人体行为的时空特征,使用Softmax分类器进行人体行为的分类识别。实验结果表明,提出的方法能够有效提取人体行为的潜在特征,不但在MSR-Action3D数据集上能够获得与当前最好方法一致的识别效果,在UTKinect-Action3D数据集也能够获得与基准项目相当的识别效果。本方法的优势是不需要人工提取特征,特征提取和分类识别构成一个端到端的完整闭环系统,方法更加简单。同时,研究方法也验证了深度卷积神经网络模型具有良好的泛化性能,使用MSR-Action3D数据集训练的模型直接应用于UTKinect-Action3D数据集上行为的分类识别,同样获得了良好的识别效果。

Action recognition based on deep convolution neural network and depth sequences

Traditional methods for action recognition include several isolated processes and depend on well-designed features, which makes them has the shotcomings of large time cost and difficult to optimize the parameters from the whole. In this paper, we use depth sequences to study deep learning-based action recognition and construct a 3D-based deep convolution neural network to automatically learn spatio-temporal features from raw depth sequences. A Softmax classifier is used on the learned features to take action recognition. Experimental results demonstrate that our method can learn feature representation automatically from depth sequences. The proposed method performs comparable results to the state-of-the-art methods on the MSR-Action3D dataset and achieves good performance in comparison to baseline methods on the UTKinect-Action3D dataset. And the proposed method is simpler in feature extracting and action recognition consist of a closed loop system which can learn features automatically. We further investigate the generalization of the trained model by transferring the learned features from one dataset (MSR-Action3D) to another dataset (UTKinect-Action3D) without retraining and obtain very promising classification accuracy.