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I will do video object tracking and bounding box for autonomous vehicles
About this Gig
Need a meticulous annotator for your computer vision models? You are in the right place!
I specialize in high-precision video object tracking and bounding box annotation, focusing on traffic monitoring, autonomous driving pipelines, and dynamic sequencing. I leverage Label Studio and CVAT to ensure structural accuracy across every frame.
What I Offer:
- Video Object Tracking: Keyframe annotation, precise tracking interpolation, and multi-class tracking pipelines.
- Bounding Boxes: Tightly grouped traffic arrays, vehicle labeling, pedestrian mapping, and high-density tracking.
- Obstruction Handling: Expert alignment for occluded objects and overlapping boundaries.
- Output Flexibility: Clean exports formatted to your needs (COCO JSON, YOLO txt, Pascal VOC).
Why Choose Me?
- Practical dataset pipeline training via DataLens Africa.
- Multi-step manual quality assurance checks to eliminate label noise.
- Clean XML custom configuration adjustments to match your templates.
Please drop me a message with your project guidelines and dataset samples before placing an order. Let's power up your ML models together!
Technique:
Manual
Tagging type:
Image
•
Video
My Portfolio
FAQ
What annotation tools do you use for dataset production?
I am fully proficient in Label Studio and CVAT for computer vision datasets. If your team relies on a custom enterprise tool or a specific cloud setup (like Roboflow or V7), I can easily integrate into your pipeline as long as you provide the login credentials and labeling guidelines.
What file formats can you deliver the completed annotations in?
I can export your completed labels in almost any standard machine learning structure you require. This includes COCO JSON, YOLO (.txt), Pascal VOC XML, CSV, and standard JSON formats. Just specify your target architecture before we start!
How do you handle complex video object tracking or occluded targets?
I manually place keyframes at points of directional change and use structural frame interpolation to ensure boxes remain perfectly bounded across motion sequences. For occluded or overlapping vehicles, I strictly follow your overlap threshold guidelines to maintain consistent unique tracking IDs.
How do you guarantee the quality and accuracy of the dataset?
I implement a strict, multi-step Manual Quality Assurance (QA) protocol. After completing a batch, I re-verify the annotation anchors layer-by-layer to entirely eliminate label noise, misclassifications, or drifting boundaries, ensuring your training data is immediately ready for model ingestion.
