3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

TL;DR

This paper introduces a novel method for human action recognition in depth videos using GLAC features extracted from motion and static images, achieving superior performance on multiple datasets.

Contribution

The paper presents a new approach combining GLAC features from MHIs and SHIs with l2-CRC classification for improved action recognition accuracy.

Findings

Outperforms existing methods on MSR-Action3D, DHA, and UTD-MHAD datasets.

Effective feature extraction using GLAC from both motion and static images.

Demonstrates robustness and high accuracy in action classification.

Abstract

In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches.