Build Your AI Surveillance Solution
AI-Powered Surveillance Hits 98% Detection Accuracy, Saves 30 Hours Monthly, and Automates 100% of Entry Logging
Case Study Summary
Executive summary
Whizzbridge built an AI-powered surveillance system to automate people counting and entry monitoring, replacing 100% of inconsistent manual logging with real-time structured data. Built on Jetson Nano with a custom YOLOv8 model optimized via TensorRT, the system achieved ~98% detection accuracy in varying lighting and moderate crowd conditions, eliminating 30 hours of manual work per month per deployment. It also provides a scalable foundation for occupancy monitoring, movement analytics, and future inventory tracking.
~98%
Detection accuracy
In varying lighting conditions
30 hrs/mo
Manual work eliminated
Per deployment
100%
Automated entry logging
vs. inconsistent manual tracking
Challenges
- 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
- ~98% detection accuracy in varying lighting and moderate crowd conditions.
- 30 hours of manual work eliminated per month in a single deployment.
- 100% automated entry logging, replacing inconsistent manual tracking with real-time structured data.
- Cost-efficient on-premise deployment on Jetson Nano hardware.
- Scalable foundation for occupancy monitoring, movement analytics, and future inventory tracking.
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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