论文标题
以应用程序驱动的AI范式进行人类行动识别
Application-Driven AI Paradigm for Human Action Recognition
论文作者
论文摘要
近年来，计算机视觉中的人类行动识别已得到广泛研究。但是，大多数算法仅考虑某些动作，即使计算成本很高。这不适用于具有多个操作的实际应用，以低计算成本来识别。为了满足各种应用程序方案，本文提出了一个由两个模块组成的统一人类行动识别框架，即多形式的人类检测和相应的作用分类。其中，构建了一个开源数据集，以训练一个多式人类检测模型，该模型区分人类的整个身体，上半身或部分身体，并采用了随后的行动分类模型来识别诸如下降，睡觉或上班或上班等的行动。一些实验性结果表明，统一的框架对各种应用程序方案有效。预计它将成为人类行动识别的新型AI范围。
Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition.
