ThreatGaze: Smart Visual Behavior Analytics for Risk Prevention

Abstract

This paper presents a Visual Behavior Analysis (VBA) framework designed to detect and interpret human activity. integrates YOLOv8 for human detection, MediaPipe Pose for posture recognition, identification. Together, these tools enable accurate differentiation between normal and suspicious behaviors. Experimental evaluation shows behavior recognition accuracy of 85–95% while maintaining 25–30 frames per second, making it suitable for real-time monitoring. The proposed approach enhances response time, supports proactive crime prevention, and provides a scalable platform for safer public environments.

Introduction

Visual Behavior Analysis (VBA) is an AI-driven field focused on interpreting human gestures, movements, and expressions from video data—addressing the limitations of manual surveillance such as fatigue and inaccuracy. With increasing urbanization, there's a critical need for automated, real-time, and scalable monitoring systems.

2. Proposed Framework

The paper proposes a real-time behavioral monitoring system that integrates:

• YOLOv8 – For fast and accurate human detection.

• MediaPipe Pose – For detailed pose estimation using skeletal landmarks.

• Haar Cascade – For facial recognition.

• Threat Detection Module – For classifying behaviors as normal or threatening.

The system provides:

• Real-time alerts (e.g., "Threat Detected" vs. "Normal")

• Visual overlays for detected threats

• Data logging for audit and analysis

3. Related Works & Literature Insights

• Traditional systems struggle in low light, occlusion, and crowded conditions.

• Gaze tracking and emotion recognition show promise but lack real-time robustness.

• Deep learning models like CNNs, LSTMs, and GCNs have improved recognition but face performance trade-offs.

Common limitations in prior systems:

• False positives due to natural behavior variations

• Inadequate generalization due to limited datasets

• Slow response time in dynamic or complex environments

4. System Architecture & Workflow

A. Key Components:

1. Video Preprocessing: Enhances input frames for clarity.

2. Human Detection: YOLOv8 identifies people in real time.

3. Pose Estimation: MediaPipe tracks body movements via skeletal points.

4. Threat Analysis: Detects suspicious behavior (e.g., aggression, sudden motion).

5. Behavior Classification: Flags threats and logs events.

6. Output: Visual feedback and database logging for reports.

B. Workflow Stages:

• Frame capture → Detection → Pose estimation → Threat classification → Real-time alert → Data storage

5. Results & Evaluation

• Accuracy: 85–95%, depending on environment.

• Threat Detection Confidence: ~90% for aggressive/abnormal actions.

• Performance: Outperforms traditional CCTV and motion detection, especially in real-time threat assessment.

6. System Requirements

Hardware:

• Multi-core CPU, GTX 1060 GPU or better

• 8–16 GB RAM, SSD (500 GB), HD IP camera (1080p)

• Network: ≥10 Mbps for real-time transmission

Software & Tools:

• OS: Ubuntu 20.04 or Windows 10/11

• Libraries: YOLOv8, MediaPipe, OpenCV, Haar Cascade

• Frameworks: TensorFlow/PyTorch, Flask

• Database: MySQL/PostgreSQL

7. Applications & Scalability

• Security monitoring

• Workplace safety

• Healthcare & behavioral studies

• Scalable design supports future integration with face recognition, multilingual support, and health analytics.

Conclusion

This research introduces a Visual Behavior Analysis system that integrates YOLOv8, MediaPipe Pose, and Haar Cascade for real-time human behavior monitoring. The framework achieves high accuracy, reliable response speed, and practical applications in public safety, healthcare, and workplace monitoring. While the system addresses many limitations of manual surveillance, challenges remain in scalability, false positives, and handling subtle behaviors. Future improvements will include sensor fusion, larger datasets, and multilingual support for wider applicability.

References

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Copyright

Copyright © 2025 Dr. Girish Kumar D, Miss. Thela Shirisha . 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.