Perception & ADAS
Object detection, lane-keeping and collision-avoidance models depend on consistent, frame-accurate labels. We keep identities stable across video so tracking stays reliable.
Sector · Mobility
Annotation for autonomous vehicles & mobility.
Perception systems are only as good as the data behind them. We label the road scenes — in image and video — that teach vehicles to see other road users, read the road, and understand what's drivable.
What we label
What we annotate.
2D & 3D bounding boxes
Lane & road segmentation
Drivable-space masks
Pedestrians & cyclists
Traffic signs & lights
Video object tracking (MOT)
LiDAR point cloud (on request)
Where it's used
How it's used.
Image and video, together
Road scenes rarely live in a single frame. We annotate sequences — not just stills — so temporal models learn motion and intent, not just appearance.
Consistency at scale
Large mobility datasets fail on inconsistency. Per-annotator quality tracking and multi-pass review keep the label taxonomy identical across millions of frames.
This is a newer domain for us, built on the same detection and tracking skills honed in agriculture. We'd start with a calibration batch to lock the taxonomy to your stack.
