Recognizing every person's action in a crowded and cluttered environment is a challenging task. In this paper, we propose a real-time action recognition method, Action4D, which gives reliable and accurate results in the real-world settings. We propose to tackle the action recognition problem using a holistic 4D "scan" of a cluttered scene to include every detail about the people and environment. Recognizing multiple people's actions in the cluttered 4D representation is a new problem. In this paper, we propose novel methods to solve this problem. We propose a new method to track people in 4D, which can reliably detect and follow each person in real time. We propose a new deep neural network, the Action4D-Net, to recognize the action of each tracked person. The Action4D-Net's novel structure uses both the global feature and the focused attention to achieve state-of-the-art result. Our real-time method is invariant to camera view angles, resistant to clutter and able to handle crowd. The experimental results show that the proposed method is fast, reliable and accurate. Our method paves the way to action recognition in the real-world applications and is ready to be deployed to enable smart homes, smart factories and smart stores.

本文提出了一种名为Action4D的实时行动识别方法，它使用全局特征和聚焦注意力相结合的创新结构进行行动识别，可在复杂的环境中进行精确而可靠的多人行动识别。该方法能够灵活适应不同的场景，对视角及影响行动识别的杂乱因素均有良好的抵抗能力，从而为实现智能家居、智能工厂和智能商店等各种智能应用提供了奠基性的方法。

Action4D:人群和杂乱环境中的实时动作识别