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2026 Vol.17, Issue 2 Preview Page

General Article

Intelligent video monitoring system for risk assessment and behavioral analysis in sustainable building environments
Mohammed Arif Hasan Chowdhury1*
,
Jeenat Sultana2
,
Shusmoy Chowdhury3
,
Mashetti Santhoshi4
,
Rajender Kumar5
,
Gaurav Sharma67
1Assistant Professor, Department of CSE, University of Science and Technology, Chittagong, Bangladesh
2Assistant Professor, Department of Computer Science and Engineering, Southern University, Bangladesh
3Research Associate, Computer Science Department, Missouri State University, United States of America
4Senior Assistant Professor, Department of Electronics and Communication Engineering, CVR College of Engineering, Hyderabad-501510, Telangana, India
5Assistant Professor, Department of Electrical and Electronics Engineering, Guru Jambheshwar University of Science and Technology, Hisar-125001, Haryana, India
6Adjunct Faculty, Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam 602105, Chennai, Tamilnadu, India
7Adjunct Faculty, Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
*Corresponding Author
30 June 2026. pp. 270-285
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Abstract

Sustainable building environments increasingly rely on intelligent monitoring systems to enhance safety, optimize facility management, and support secure urban infrastructure. This study presents an intelligent video monitoring framework for behavioral analysis and risk assessment within sustainable building environments using classical machine learning techniques. The proposed non-invasive system analyzes visual cues derived from facial and hand gestures captured through surveillance video streams. Nine behavioral features were extracted and categorized into facial and hand gesture modalities. Seven supervised classification algorithms—Logistic Regression, Decision Tree, k-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes, Support Vector Machine (SVM), and Random Forest—were implemented to evaluate system performance. Comparative analysis demonstrates that KNN and SVM achieved the highest overall accuracy of approximately 80% when multimodal gesture features were fused. Individually, facial gestures yielded up to 79% accuracy using LDA, while hand gestures achieved 72% accuracy. The results indicate that classical machine learning methods can effectively support behavioral risk detection in smart building environments. The proposed framework offers a cost-effective and scalable solution that can be integrated into sustainable building management systems to enhance safety monitoring and contribute to resilient urban infrastructure.

Keywords
intelligent video monitoring
sustainable building environments
behavioral analysis
risk assessment
machine learning
smart building systems
facility management
urban infrastructure safety
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 17
  • No :2
  • Pages :270-285
  • Received Date : 2026-03-04
  • Accepted Date : 2026-03-22
  • DOI :https://doi.org/10.22712/susb.20260016
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