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Recognizing every person's action in a crowded and cluttered environment is achallenging task. In this paper, we propose a real-time action recognitionmethod, Action4D, which gives reliable and accurate results in the real-worldsettings. We propose to tackle the action recognition problem using a holistic4D "scan" of a cluttered scene to include every detail about the people andenvironment. Recognizing multiple people's actions in the cluttered 4Drepresentation is a new problem. In this paper, we propose novel methods tosolve this problem. We propose a new method to track people in 4D, which canreliably detect and follow each person in real time. We propose a new deepneural network, the Action4D-Net, to recognize the action of each trackedperson. The Action4D-Net's novel structure uses both the global feature and thefocused attention to achieve state-of-the-art result. Our real-time method isinvariant to camera view angles, resistant to clutter and able to handle crowd.The experimental results show that the proposed method is fast, reliable andaccurate. Our method paves the way to action recognition in the real-worldapplications and is ready to be deployed to enable smart homes, smart factoriesand smart stores.

Action4D: Real-time Action Recognition in the Crowd and Clutter