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Share Your Data
Upload raw images, video, text, audio, or LiDAR securely — we ingest from cloud storage, SFTP, or your existing ML pipeline.
Frame-accurate video annotation — object tracking, event detection, and temporal labels for surveillance, sports analytics, and autonomous vehicle datasets.
Video models require labels that stay consistent across frames — not just static boxes on still images. Our video annotation services provide object tracking, event detection, activity recognition, and temporal taxonomy tags for surveillance platforms, sports analytics, retail behavior AI, and autonomous vehicle perception stacks.
ID switches and drift between frames destroy tracker performance in production. Our annotators maintain object identity across sequences, annotate events on timelines, and follow frame-interval QA so your models learn stable temporal patterns.
Single-object and multi-object tracking; action and event segmentation; pose and keypoint tracks; lane and zone polygons on dashcam footage; person re-identification labels; basket and queue analytics for retail video AI.
CCTV threat detection and loitering alerts, football and basketball player tracking, in-store shopper journey analysis, warehouse forklift safety monitoring, and multi-sensor AV rigs capturing urban driving scenes.
We staff dedicated video pools with 24/7 coverage for continuous ingest from global camera networks. Projects scale from pilot clips to millions of annotated frames per program without sacrificing review depth.
Nth-frame audit sampling, consensus on difficult clips, and automated checks for temporal ID consistency. Safety-critical AV and security workloads receive additional specialist review layers.
Get frame-accurate video training data with enterprise QA. Tell us your frame rate, camera count, taxonomy, and accuracy targets — we scope video annotation timelines and pricing for your ML roadmap.
A proven calibration-to-production workflow for enterprise annotation programs.
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Upload raw images, video, text, audio, or LiDAR securely — we ingest from cloud storage, SFTP, or your existing ML pipeline.
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We define labeling guidelines, class taxonomy, edge cases, and accuracy targets with your ML and product stakeholders.
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Trained annotators label bounding boxes, masks, tracks, transcripts, or 3D cuboids in your toolchain or our workspace.
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Multi-pass review, consensus scoring, and automated checks before any dataset reaches your training jobs.
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Receive COCO, JSON, Pascal VOC, or custom exports — plus ongoing support as your models and taxonomies evolve.
Answers about scope, quality, tooling, and delivery.
Both. We label continuous frame sequences for tracking, sampled frames for classification, and event segments on timelines for behavior analytics.
Yes. We run frame-sampling QA and temporal ID consistency checks across full matches and multi-camera broadcast feeds.
Your toolchain or our secure workspace — with exports compatible with major training frameworks and custom AV perception pipelines.
Talk to our enterprise team about volume, timeline, QA targets, and pricing.