Skeleton-Based Activity Recognition: Preprocessing and Approaches

Book chapter


Sarker, S., Rahman, S., Hossain, T., Faiza Ahmed, S., Jamal, L. and Ahad, M. 2021. Skeleton-Based Activity Recognition: Preprocessing and Approaches. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 48-81
AuthorsSarker, S., Rahman, S., Hossain, T., Faiza Ahmed, S., Jamal, L. and Ahad, M.
EditorsAhad, M., Mahbub, U. and Rahman, T.
Abstract

Research in Activity Recognition is one of the thriving areas in the field of computer vision. This development comes into existence by introducing the skeleton-based architectures for action recognition and related research areas. By advancing the research into real-time scenarios, practitioners find it fascinating and challenging to work on human action recognition because of the following core aspects—numerous types of distinct actions, variations in the multimodal datasets, feature extraction, and view adaptiveness. Moreover, hand-crafted features and depth sequence models cannot perform efficiently on the multimodal representations. Consequently, recognizing many action classes by extracting some smart and discriminative features is a daunting task. As a result, deep learning models are adapted to work in the field of skeleton-based action recognition. This chapter entails all the fundamental aspects of skeleton-based action recognition, such as—skeleton tracking, representation, preprocessing techniques, feature extraction, and recognition methods. This chapter can be a beginning point for a researcher who wishes to work in action analysis or recognition based on skeleton joint-points.

Book titleContactless Human Activity Analysis
Page range48-81
Year2021
PublisherSpringer, Cham
Publication dates
Online24 Mar 2021
Publication process dates
Deposited26 Jul 2023
Edition1
SeriesIntelligent Systems Reference Library
ISBN9783030685904
9783030685898
ISSN1868-4394
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-68590-4_2
Web address (URL)https://link.springer.com/book/10.1007/978-3-030-68590-4
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