论文标题
用于手持操作检测的应用程序驱动的AI范式
Application-Driven AI Paradigm for Hand-Held Action Detection
论文作者
论文摘要
在实际应用中，尤其是在安全要求下，需要密切监控某些手持式动作，包括吸烟，拨号，饮食等。以吸烟为例，现有的烟雾检测算法通常只用手作为目标对象检测香烟或香烟，这会导致准确的准确性。在本文中，我们提出了一个基于层次对象检测的手持操作检测的应用程序驱动的AI范式。它是一个由两个模块组成的粗到1个分层检测框架。第一个是一个粗糙的检测模块，其姿势由整个手，香烟和头部作为目标对象组成。随后的第二个是一个精细的检测模块，手指握着香烟，嘴巴和整个香烟作为目标。一些实验是通过从现实世界情景中收集的数据集进行的，结果表明，所提出的框架在复杂环境中以良好的适应性和鲁棒性实现了更高的检测率。
In practical applications especially with safety requirement, some hand-held actions need to be monitored closely, including smoking cigarettes, dialing, eating, etc. Taking smoking cigarettes as example, existing smoke detection algorithms usually detect the cigarette or cigarette with hand as the target object only, which leads to low accuracy. In this paper, we propose an application-driven AI paradigm for hand-held action detection based on hierarchical object detection. It is a coarse-to-fine hierarchical detection framework composed of two modules. The first one is a coarse detection module with the human pose consisting of the whole hand, cigarette and head as target object. The followed second one is a fine detection module with the fingers holding cigarette, mouth area and the whole cigarette as target. Some experiments are done with the dataset collected from real-world scenarios, and the results show that the proposed framework achieve higher detection rate with good adaptation and robustness in complex environments.
