  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.
