Application-Driven AI Paradigm for Human Action Recognition

TL;DR

This paper introduces a unified, application-driven AI framework for human action recognition that efficiently handles multiple actions and body forms, suitable for practical scenarios with low computational costs.

Contribution

It proposes a novel unified framework combining multi-form human detection and action classification, supported by an open-source dataset, advancing practical human action recognition.

Findings

Effective in recognizing various actions across scenarios

Uses a multi-form detection model distinguishing body parts

Achieves low computational cost for real-world applications

Abstract

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.