Intelligent Video Analytics for Human Action Recognition: The State of Knowledge-Reference-Cited by-同舟云学术

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

Published:2023-04-25 Issue:9 Volume:23 Page:4258
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Kulbacki Marek¹²^ORCID,Segen Jakub¹²^ORCID,Chaczko Zenon²³^ORCID,Rozenblit Jerzy W.⁴^ORCID,Kulbacki Michał²^ORCID,Klempous Ryszard⁵^ORCID,Wojciechowski Konrad¹^ORCID

Affiliation:

1. Polish-Japanese Academy of Information Technology, 02-008 Warsaw, Poland

2. DIVE IN AI, 53-307 Wroclaw, Poland

3. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo 2007, Australia

4. Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA

5. Wrocław University of Science and Technology, 50-370 Wroclaw, Poland

Abstract

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

https://www.mdpi.com/1424-8220/23/9/4258/pdf

Reference259 articles.

1. Research, D. (2022). Global Surveillance Camera Market: Analysis by System Type (Analog, IP Commercial, IP Consumer and Other Surveillance Camera), by Technology (Image Signal Processor, Vision Processor, Vision Processor + AI) by Region Size and Trends with Impact of COVID-19 and Forecast up to 2027, Daedal Research. Technical Report DAR17374302.

2. Davis, L.S. (2001). Real Time Computer Surveillance for Crime Detection, University of Maryland. Technical Report.

3. Lyon, D. (2007). Surveillance Studies: An Overview, Polity.

4. Ratcliffe, J. (2011). Response Guides Series Problem-Oriented Guides for Police Video Surveillance of Public Places, Center for Problem-Oriented Policing, Inc.

5. A review of video surveillance systems;Elharrouss;J. Visual Commun. Image Represent.,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion;Sensors;2024-01-29

2. Spatial-Temporal Information-Based Littering Action Detection in Natural Environment;Communications in Computer and Information Science;2023-10-31