Original scientific paper

https://doi.org/10.17559/TV-20240708001834

Human Action Recognition Using Explainable Features and Sparse Motion History Images

Wei Yang ; Jiangxi University of Technology, The Center of Collaboration and Innovation, Nanchang, China 330098
Yitong Zhou ; Jiangxi University of Chinese Medicine, The College of Computer Science, Nanchang, China 330004
Jianying Xiong ; Jiangxi University of Chinese Medicine, The College of Computer Science, Nanchang, China 330004
Shiwei Zhang ; The Hanlin Hangyu (Tianjin) Industrial Co., Ltd. Tianjin, China 301899
Lei Zhang ; The Hanlin Hangyu (Tianjin) Industrial Co., Ltd. Tianjin, China 301899
Leiyue Yao ; Jiangxi University of Chinese Medicine, The College of Computer Science, Nanchang, China 330004 *

* Corresponding author.

Abstract

This study proposes a novel approach to human action recognition (HAR) via depth sensor data. We introduce explainable features derived from skeleton sequences and a sparse motion history image (SMHI) to effectively capture motion characteristics. Our method addresses the limitations of current approaches by reducing the computational requirements while maintaining high accuracy. We propose a SlowFast-like network that combines these features for efficient HAR. Experiments on three datasets demonstrate the effectiveness of our approach, which achieves competitive accuracy with fewer features. The method also ensures user privacy by relying solely on skeleton data. This research contributes to the theoretical advancement of HAR and its practical application in various fields.

Keywords

data augmentation; human action recognition, motion feature matrix; skeleton-based HAR, video encoding

Hrčak ID:

335046

URI

https://hrcak.srce.hr/335046

Publication date:

30.8.2025.

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