Cargando…
Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization
The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analys...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739222/ https://www.ncbi.nlm.nih.gov/pubmed/36502084 http://dx.doi.org/10.3390/s22239383 |
_version_ | 1784847751868055552 |
---|---|
author | Mumtaz, Nadia Ejaz, Naveed Aladhadh, Suliman Habib, Shabana Lee, Mi Young |
author_facet | Mumtaz, Nadia Ejaz, Naveed Aladhadh, Suliman Habib, Shabana Lee, Mi Young |
author_sort | Mumtaz, Nadia |
collection | PubMed |
description | The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers’ attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique: control charts for surveillance video data analysis. The control charts concept is merged with a novel deep learning-based violence detection framework. Different from the existing methods, the proposed technique considers the importance of spatial information, as well as temporal representations of the input video data, to detect human violence. The spatial information are fused with the temporal dimension of the deep learning model using a multi-scale strategy to ensure that the temporal information are properly assisted by the spatial representations at multi-levels. The proposed frameworks’ results are kept in the history-maintaining module of the control charts to validate the level of risks involved in the live input surveillance video. The detailed experimental results over the existing datasets and the real-world video data demonstrate that the proposed approach is a prominent solution towards automated surveillance with the pre- and post-analyses of violent events. |
format | Online Article Text |
id | pubmed-9739222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97392222022-12-11 Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization Mumtaz, Nadia Ejaz, Naveed Aladhadh, Suliman Habib, Shabana Lee, Mi Young Sensors (Basel) Article The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers’ attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique: control charts for surveillance video data analysis. The control charts concept is merged with a novel deep learning-based violence detection framework. Different from the existing methods, the proposed technique considers the importance of spatial information, as well as temporal representations of the input video data, to detect human violence. The spatial information are fused with the temporal dimension of the deep learning model using a multi-scale strategy to ensure that the temporal information are properly assisted by the spatial representations at multi-levels. The proposed frameworks’ results are kept in the history-maintaining module of the control charts to validate the level of risks involved in the live input surveillance video. The detailed experimental results over the existing datasets and the real-world video data demonstrate that the proposed approach is a prominent solution towards automated surveillance with the pre- and post-analyses of violent events. MDPI 2022-12-01 /pmc/articles/PMC9739222/ /pubmed/36502084 http://dx.doi.org/10.3390/s22239383 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mumtaz, Nadia Ejaz, Naveed Aladhadh, Suliman Habib, Shabana Lee, Mi Young Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title | Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title_full | Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title_fullStr | Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title_full_unstemmed | Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title_short | Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization |
title_sort | deep multi-scale features fusion for effective violence detection and control charts visualization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739222/ https://www.ncbi.nlm.nih.gov/pubmed/36502084 http://dx.doi.org/10.3390/s22239383 |
work_keys_str_mv | AT mumtaznadia deepmultiscalefeaturesfusionforeffectiveviolencedetectionandcontrolchartsvisualization AT ejaznaveed deepmultiscalefeaturesfusionforeffectiveviolencedetectionandcontrolchartsvisualization AT aladhadhsuliman deepmultiscalefeaturesfusionforeffectiveviolencedetectionandcontrolchartsvisualization AT habibshabana deepmultiscalefeaturesfusionforeffectiveviolencedetectionandcontrolchartsvisualization AT leemiyoung deepmultiscalefeaturesfusionforeffectiveviolencedetectionandcontrolchartsvisualization |