Enabling Intelligent Entry Monitoring with an AI-Powered Surveillance System
Industries
Retail, Information Technology
Services
AI Surveillance Development, Computer Vision Enablement
Tools We used
Jetson Nano, YOLOv8, TensorRT, REST API, MQTT, On-Premise Edge AI Deployment
Challenges We Faced
- Traditional CCTV systems provided visual monitoring, but lacked the intelligence needed for automated people counting and real-time structured data.
- Manual entry logging and human monitoring were inconsistent, time-consuming, and not scalable for operational environments.
- Businesses needed a reliable way to detect and count people entering controlled spaces without increasing manpower or depending on manual processes.
- The solution had to work accurately in real-world conditions, including varying lighting and moderate crowd movement.
What we needed was not just surveillance, but a smarter system that could turn movement into actionable operational data.
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Whizzbridge’s Solution
- Designed and developed a lightweight, real-time AI-powered entry detection system for monitoring people entering predefined zones.
- Built the solution on Jetson Nano, enabling cost-efficient on-premise edge AI deployment with conventional video camera hardware.
- Trained a custom object detection model using YOLOv8 and optimized it with TensorRT for efficient real-time inference.
- Implemented a virtual detection line to trigger entry events whenever a person crossed into a defined area.
- Created single-count logic to ensure each person was counted only once per entry, preventing duplicate counts caused by repeated motion in the frame.
- Enabled seamless integration by exposing entry count data through REST API and MQTT, making the system compatible with dashboards, alerts, and analytics platforms.
Results We Achieved
- Achieved approximately 98% detection accuracy, even in varying lighting and moderate crowd conditions.
- Eliminated the need for manual entry logging or additional staff for tracking movement.
- Replaced nearly 30 hours per month of manual logging work in a single deployment.
- Delivered a cost-effective implementation with minimal recurring maintenance requirements.
- Created a scalable architecture that can expand to multiple entry points, real-time occupancy tracking, and direction-based movement analytics.
- Extended the system’s future potential by making the same architecture adaptable for stock counting and inventory movement tracking in industrial environments.
The result was a practical AI surveillance solution that improved accuracy, reduced manual effort, and created a scalable foundation for smarter operations.
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