2D SKELETON-BASED HUMAN ACTION RECOGNITION USING ACTION-SNIPPET REPRESENTATION AND DEEP SEQUENTIAL NEURAL NETWORK
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Date
2022-05
Authors
Askar, Aizada
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Human action recognition is one of the crucial and important tasks in data science. It
aims to understand human behavior and assign a label on performed action and has
diverse applications. Domains, where this application is used, includes visual surveillance,
human–computer interaction and video retrieval. Hence, discriminating human
actions is a challenging problem with a lot of issues like motion performance, occlusions
and dynamic background, and different data representations. There are many
researches that explore various types of approaches for human action recognition. In
this work we propose advanced geometric features and adequate deep sequential neural
networks (DSNN) for 2D skeleton-based HAR. The 2D skeleton data used in this
project are extracted from RGB video sequences, allowing the use of the proposed
model to enrich contextual information. The 2D skeleton joint coordinates of the
human are used to capture the spatial and temporal relationship between poses. We
employ BiLSTM and Transformer models to classify human actions as they are capable
of concurrently modeling spatial relationships between geometric characteristics
of different body parts.
Description
Keywords
visual surveillance, Type of access: Open Access, human action recognition, deep sequential neural networks, DSNN, 2D skeleton-based HAR, HAR, BiLSTM, Research Subject Categories::TECHNOLOGY
Citation
Askar, A. (2022). 2D skeleton-based Human Action Recognition using Action-Snippet Representation and Deep Sequential Neural Network (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan