Continuous Human Action Recognition for Human-Machine Interaction: A Review

TL;DR

This review paper analyzes recent methods for continuous human action recognition in videos, emphasizing challenges, techniques, and future research directions for real-time human-machine interaction applications.

Contribution

It provides a comprehensive comparison of action segmentation methods, discusses the impact of detection techniques, and highlights key challenges and future research directions.

Findings

Analysis of various action segmentation techniques

Impact of object detection and tracking on performance

Identification of limitations and future research directions

Abstract

With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action transitions within an input video are challenging but necessary tasks for applications that require real-time human-machine interaction. By reviewing a large body of recent related work in the literature, we thoroughly analyse, explain and compare action segmentation methods and provide details on the feature extraction and learning strategies that are used on most state-of-the-art methods. We cover the impact of the performance of object detection and tracking techniques on human action segmentation methodologies. We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation and deployment.