Application-Driven AI Paradigm for Hand-Held Action Detection
This work addresses safety monitoring needs in practical applications, but it is incremental as it builds on existing detection methods with a hierarchical approach.
The paper tackles the problem of low accuracy in detecting hand-held actions like smoking by proposing a hierarchical object detection framework, achieving higher detection rates with good adaptation and robustness in complex environments.
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.