Computer Vision System · Object Detection
Real-time computer vision system for object detection and tracking with edge deployment and cloud inference capabilities.
Case study
Computer Vision System · Object Detection
Real-time computer vision system for object detection and tracking with edge deployment and cloud inference capabilities.
Real-time computer vision system for object detection and tracking with edge deployment and cloud inference.
The Challenge
The client needed a Real-time computer vision system for object detection and tracking with edge deployment and cloud inference. but lacked the technical expertise or resources to build it effectively.
The Solution
Real-time computer vision system for object detection and tracking with edge deployment and cloud inference.
Key results
Detection accuracy
Frames per second
Edge devices deployed
Stack
Timeline
Week 1–2
Model selection, dataset preparation, and edge deployment strategy
Week 3–6
Model training, optimization, and edge deployment pipeline
Week 7–8
Cloud inference setup, monitoring, and performance tuning
“The edge deployment reduced latency by 80% while maintaining high accuracy.”
Project Details
Technical implementation and architecture overview
Object detection
Trained and optimized YOLO models for real-time object detection with custom dataset and transfer learning.
Edge deployment
Deployed optimized models to edge devices with TensorFlow Lite, achieving real-time inference at 30+ FPS.
Hybrid inference
Built hybrid architecture with edge inference and cloud fallback for complex scenarios and batch processing.
FAQs
What do you build? +
Web3, AI, Systems, Web. End-to-end. One person. From idea to deployed.
Do you do consultancy? +
Yes. Architecture, stack selection, code reviews. Hourly or contract. Get unstuck fast.
How fast can you deliver? +
Fast. I focus on going live. Less bureaucracy, more shipping. Let's discuss timeline.
One person for everything? +
Yes. Frontend, backend, infrastructure, deployment. Complete systems. End-to-end.