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I will deploy yolov8 rtsp object tracking as a fastapi backend
AI Engineer for Computer Vision and Generative AI
I am an expert AI Engineer & Python Developer architecting scalable, end-to-end AI solutions. I bridge the gap between Computer Vision and Generative AI.
Core Expertise:
• Computer Vision: Real-time ...
About this Gig
Are you struggling to deploy YOLOv8 RTSP object tracking into a reliable, production-ready FastAPI backend?
Most computer vision scripts work perfectly in a Jupyter notebook, but the moment you connect them to live RTSP camera feeds, they drop frames, lose tracking IDs, or crash the server.
What I Build:
- Live RTSP Processing: Seamless IP camera integration handling dropped frames.
- Advanced Tracking: ByteTrack/BoT-SORT for consistent multi-object IDs in crowded scenes (traffic/retail).
- Edge-Case Optimization: SAHI for small objects & night/angle preprocessing.
- Production Deployment: Clean FastAPI backend containerized via Docker.
- Inference Acceleration: ONNX/TensorRT optimization for constrained GPUs.
Tech Stack:
Python | YOLO | OpenCV | FastAPI | Docker | ONNX | CUDA
Why Me?
I don't just train models; I engineer resilient systems. With professional MLOps experience, I deliver scalable code built for actual business integration, not fragile experiments.
Important: Hardware & camera setups vary. Please message me before ordering to discuss FPS, GPU constraints, and architecture.
Programming language:
Python
•
Colab
•
MLflow
•
Amazon SageMaker
Frameworks:
Scikit-learn
•
Google ML Kit
•
Keras
•
PyTorch
•
Panda
My Portfolio
FAQ
What hardware do I need to run this RTSP pipeline smoothly?
It depends on the number of concurrent streams and desired FPS. For optimal performance, I recommend an NVIDIA GPU. To maximize your hardware's capabilities, I optimize the models using ONNX Runtime or TensorRT, ensuring efficient inference even on constrained resources.
Can your system handle multiple RTSP streams simultaneously?
Yes. I design the pipeline with efficient multi-threading and batching. When wrapped in a FastAPI backend, the system can handle concurrent RTSP streams, provided your deployment hardware has the compute capacity to support the load.
My cameras are mounted very high. Can you detect small objects accurately?
Yes. For steep angles or distant objects, standard YOLOv8 often struggles. I implement SAHI (Slicing Aided Hyper Inference) alongside custom preprocessing pipelines to accurately detect and track small objects (like distant vehicles or crowds) without losing frame rate.
How do you handle tracking IDs when cameras lag or drop frames?
RTSP streams are notoriously unstable in real-world networks. I utilize robust tracking algorithms like ByteTrack and BoT-SORT, combined with custom logic to maintain ID consistency even if the camera stutters or drops frames temporarily.
How will I receive the final deliverable?
You will not just get a raw Python script. You will receive a fully containerized Docker image with a FastAPI backend, clear API documentation, and instructions for spinning it up on your local server or cloud instance.
Do I need to provide the dataset for custom object detection?
For standard objects (cars, people, etc.), we can use pre-trained YOLOv8 weights. If you need to track custom/niche objects, you will need to provide annotated data. If you have raw images, I can assist with automated image annotation tooling to speed up the process.
