Camera Image Training Data for Autonomous Perception

It is the preparation of 2D labels in camera images for object detection, semantic segmentation, and scene understanding. Unitlab helps teams curate, annotate, and review those training examples.

Your ontology can include vehicles, pedestrians, cyclists, traffic signs, lights, construction objects, and relevant scene regions. Choose classes that match the intended perception task.

Yes. Annotators can adjust geometry and record structured visibility or occlusion properties. Define whether a boundary follows the visible portion or another agreed convention before work begins.

This workflow covers 2D camera images and their supported geometry. Use it to prepare image-based perception datasets with explicit classes, boundaries, and scene properties.

Still-image labels describe objects and regions in one frame. Traffic-video annotation adds persistent tracks and changing frame-level properties when the model must understand motion over time.

Define shared classes, structured properties, and clear labeling instructions before work starts. Use representative examples and contextual review to resolve disagreements in the camera-based perception dataset.

Yes. Route image annotation through Review and Rework stages. Reviewers can inspect the source data, correct labels, and send an item back when more work is needed.

Yes. Use dataset search, metadata, tags, and available filters to select relevant image assets. Keep representative conditions and difficult examples visible in the preparation workflow.

Export reviewed image annotations in a supported format appropriate to the label types. Dataset versions help teams identify which prepared examples belong to the training or evaluation release.