[2006.07743] 3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information

Human actions recognition is a fundamental task in artificial vision, that has earned a great importance in recent years due to its multiple applications in different areas. %, such as the study of human behavior, security or video surveillance. In this context, this paper describes an approach for real-time human action recognition from raw depth image-sequences, provided by an RGB-D camera. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from depth sequences without %any costly pre-processing. Furthermore, the described 3D-CNN allows %automatic features extraction and actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people's privacy% allows recognizing the actions carried out by people, protecting their privacy%\sout{of them} , since their identities can not be recognized from t

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@allowfirm 3DFCNNの論文( t.co/ozhIvOK4Od )で、3D CNNを組み込んだネットワークの推論時間を探していたら、19枚目のTable 5に「30フレームを入力として0.09秒」とあって、ちょっとびっくりしました。8枚目のTable 1のネットワーク構造を見ると、計算時間を抑えるためコンパクトにしている雰囲気が。

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