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I will do yolo object detection for recognition, tracking, counting and computer vision
Computer Vision Engineer YOLO, Object Tracking, AI, and LIDAR Developer
Hello, I’m Bartholomaus, a Computer Vision, BIM Automation & Spatial Computing Developer with 5+ years of experience building production-ready engineering and vision solutions.
I develop custom Revi...
About this Gig
A detection model that works in a notebook but fails on real video streams costs more than money. It costs trust, deadlines, and the client who was counting on results.
We specialize in Python-based AI computer vision using advanced technologies like YOLO (v3v8), MediaPipe, and other powerful frameworks to build object detection, recognition, and tracking solutions.
I offer custom solutions for detecting and tracking objects in images, videos, and live streams. Whether it's face recognition, object classification, or real-time tracking, Ill provide accurate and efficient results.
What i Develop:
- Object tracking
- Multi-object tracking (MOT)
- Real-time video tracking
- Person and vehicle tracking
- Object counting and movement analysis
- YOLO + tracking integration
- DeepSORT / BoT-SORT integration
- OpenCV video tracking
- CCTV and RTSP tracking
- Tracking model optimization
Technologies:
- YOLO, MediaPipe, OpenCV, Python, PyTorch, TensorFlow
Previous projects include: People & Vehicle Detection, Traffic Analytics, Warehouse Monitoring, Medical Imaging, Smart Surveillance, and Sports Tracking systems.
Message me for a consultation before placing your order.
Programming language:
Python
•
R
•
MATLAB
•
SQL
•
Colab
Tools:
Jupyter Notebook
•
OpenCV
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Amazon SageMaker
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CVAT
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Colab
•
PyTorch
Frameworks:
Scikit-learn
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Google ML Kit
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SimpleCV
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PyTorch
•
Panda
FAQ
1. What do you need to get started?
The main requirements are your project objective, target objects, sample images or videos, camera/source details, expected output, and deployment environment. If you already have a dataset, YOLO model, trained weights, Python code, IP camera, or existing application, those can also be provided.
2. How long does a YOLO object detection project take?
A straightforward detection integration can take around 5 days, while systems involving tracking, counting, IP cameras, custom datasets, segmentation, or advanced video analytics can take 1–3 weeks or longer depending on complexity.
3. How many revisions are included?
Each package includes a defined number of revisions. Revisions cover adjustments within the original agreed scope. New object classes, additional camera systems, major workflow changes, or new computer vision functionality may require a custom order.
4. What tools and technologies are used?
Depending on the project, the technology stack can include YOLO, OpenCV, Python, PyTorch, TensorFlow, deep learning, machine learning, computer vision, image processing, object tracking frameworks, segmentation models, IP cameras, RTSP streams, and GPU acceleration.
5. Can you build a completely custom computer vision system?
Yes. Custom projects can combine object detection, tracking, counting, segmentation, face recognition, pose estimation, IP camera monitoring, video analytics, alerts, dashboards, and application integration.
