Title:
Deep Learning-based Person Tracking: A Smart Approach to Security and Civic Monitoring
Authors:
Shailendra Singh Kathait – Co-Founder & Chief Data Scientist, Ashish Kumar – Principal Data Scientist, Samay Sawal – Intern Data Scientist, Ram Patidar – Data Scientist, Khushi Agrawal – Intern Data Scientist [all Valiance Solutions Noida, India]
Summary:
This paper introduces a deep learning-based framework designed for real-time detection and surveillance of individuals violating designated restricted zones, such as vehicle-only areas. Utilizing advanced object detection algorithms, specifically YOLOv8, the system focuses on head detection and spatial reasoning to accurately track individuals entering these zones. A centroid-based tracking mechanism ensures each individual is flagged only once per frame, enhancing detection precision.
To further improve accuracy, the framework incorporates modifications to bounding boxes and employs region-specific polygonal filtering, allowing for more precise violation detection. Visual feedback is provided through overlaying boundary boxes and labels on detected individuals, while cumulative violation counts are recorded for monitoring purposes.
The proposed system demonstrates stable performance under varying conditions, making it suitable for applications in crowd management, security, and surveillance. Its flexible architecture allows for the integration of additional capabilities, such as movement direction and speed analysis, to provide more context-aware violation assessments. By leveraging existing surveillance infrastructure, this approach offers a cost-effective solution for enhancing urban safety and monitoring.
