Visual Behavior Analysis

Abstract

With rising crime rates and increasing security concerns, intelligent surveillance has become crucial for ensuring public safety.This paper presents a Visual Behavior Analysis system that automates crime detection and tracking using real-time video surveillance. The system employs You Only Look Once (YOLO) is a deep learning algorithm recognized for its speed and accuracy in multi-object detection, to monitor public spaces efficiently. By analysing movement patterns and identifying anomalies, it can detect potential threats and generate instant alerts to prevent criminal activities. Additionally, the system integrates facial recognition through the use of Haar Cascade classifiers to assist law enforcement in identifying individuals involved in suspicious activities. It also detects objects such as weapons or unusual postures, improving the accuracy of crime prediction. The system processes live video feeds, minimizing response time and reducing reliance on manual monitoring. By leveraging computer vision and artificial intelligence, improving security measures by offering proactive crime prevention and real-time situational awareness. It goes beyond just aids in immediate threat detection but also supports post-incident investigations by preserving crucial evidence. The proposed system provides a scalable, efficient, and automated solution for modern surveillance challenges, contributing to a safer and smarter security infrastructure.

Introduction

Visual Behavior Analysis (VBA) is an advancing field in computer vision and AI focused on interpreting human actions, gestures, and expressions in real time through video surveillance. Using techniques like deep learning, pose estimation, and facial analysis, VBA systems detect and classify behaviors as normal or threatening, enhancing security, healthcare, and workplace safety.

Key Applications:

• Public Safety: Automated detection of suspicious or aggressive behavior improves response times and crime prevention in public spaces.

• Workplace Monitoring: Detects employee stress or fatigue to promote well-being.

• Healthcare: Monitors patients for cognitive or physical impairments, aiding early diagnosis.

Research Objectives:

1. Develop a high-accuracy, adaptable computer vision system for complex video input.

2. Design an efficient pipeline for real-time video analysis with low latency.

3. Use advanced AI models to classify human behavior (normal, stressed, threatening) to improve security and monitoring.

Related Works Overview:

• Deep learning models (CNNs, RNNs, transformers) have improved action recognition but face challenges with real-world variability and computational demands.

• Tools like Providence aid behavioral analysis but lack robustness in uncontrolled environments and edge device optimization.

• Social Signal Processing (SSP) integrates nonverbal cues but needs updating with recent AI advances.

• YOLO-based systems show strong performance in anomaly and crime detection, balancing accuracy and efficiency, yet require better real-world adaptability and hardware optimization.

Proposed Method:

• Combines YOLOv8 for human detection, MediaPipe Pose for pose estimation, and OpenCV for video processing.

• Processes real-time video streams to classify behavior as normal or threatening and sends instant alerts.

• Architecture supports scalability and future enhancements like healthcare integration and multilingual alerts.

Results & Discussion:

• Achieved 25–30 FPS processing speed and 85–95% accuracy in behavior classification.

• Effectively detects threats with 90% confidence, outperforming traditional CCTV by automating detection and reducing response time.

• Uses pose estimation for superior accuracy in crowded or low-light settings compared to motion-based methods.

• Future improvements planned in scalability, subtle threat detection, and sensor fusion.

Conclusion

The VBA system offers an automated, real-time solution for behavior analysis across security, healthcare, and workplace domains, enhancing proactive threat detection and intervention. Continued development will improve adaptability, precision, and deployment efficiency.

References

[1] Vattikunta Mahitha, Allenki Usha Reddy, Jangili Sunitha, Dr. P. Rama, \"Human Behavior and Abnormality Detection Using YOLO and Conv2D,\" International Journal of Scientific Development and Research (IJSDR), Volume 8 Issue 4, April 2023, pp. 1009-1016, DOI: 10.1109/IJSDR.2023.1234567. [2] K. Ganagavalli, V. Santhi, \"YOLO-based Anomaly Activity Detection System for Human Behavior Analysis and Crime Mitigation,\" Signal, Image and Video Processing, 2024, DOI: 10.1007/s11760-024-03164-7. [3] Marco Cristani, R. Raghavendra, Alessio Del Bue, Vittorio Murino, \"Human Behavior Analysis in Video Surveillance: A Social Signal Processing Perspective,\" Neurocomputing Journal, 2011, DOI: 10.1016/j.neucom.2011.12.006. [4] Riku Arakawa, Kiyosu Maeda, Hiromu Yakura, \"Providence: Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos,\" ACM, 2024, DOI: 10.1145/nnnnnnn.nnnnnnn. [5] Hieu H. Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A. Velastin, \"Video-based Human Action Recognition using Deep Learning: A Review,\" arXiv preprint arXiv:2208.03775, 2022.

Copyright

Copyright © 2025 Dr. Mallikarjuna A, A Shruthi, G Anjali , A Mohammed Anas, J Aishwarya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.