  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.
