Bounding boxes
Localizing and classifying objects quickly with tight rectangular boundaries, essential for object detection and tracking.
Video Labeling Services
Video Annotation
Frame-by-frame annotation, persistent object tracking, and temporal event labeling for world-class video AI models.
RUN FREE PILOTFeatured case study
Video Annotation
Temporal Object Tracking & Multi-Camera AI
Trusted by leading brands
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Your data problem
has a human solution.
Autonomous Driving Teams
Persistent tracking across long-form dash-cam sequences requires specialists who understand occlusion and re-IDWe annotate frame-by-frame at scale, maintaining object identity through intersections, weather, and night conditions
Physical Surveillance AI
Multi-camera tracking with consistent IDs across view handoffs is a challenge we're built to solveFrom retail shrinkage detection to perimeter security, we label every relevant event type your model needs
Sports Analytics Platforms
Player tracking, ball detection, and event detection across dynamic, fast-moving game footageWe understand the physics and tempo of each sport — labels reflect game context, not just geometric boxes
Robotics Engineers
Your manipulation and navigation models need temporal annotations that hold up across action sequencesWe deliver keyframe-anchored annotations with semantic event labels for robust robot policy training
OUR ANNOTATION SERVICES FOR COMPUTER VISION & VIDEO DATA
We provide high-precision annotation for all major computer vision tasks and video datasets. Our global team can expertly deliver the following annotation services:
Bounding boxes
Localizing and classifying objects quickly with tight rectangular boundaries, essential for object detection and tracking.
A Futureproofed Pipeline
We exist for a future where temporal video models are trained on precisely annotated, expertly curated frame sequences running perfectly in your infrastructure.
Step 1
Free Pilot
Step 2
QA Review
Step 3
Proposal
Step 4
Start Labeling
Step 5
Delivery
Estimate Your Costs for ML projects
1 Pick application field
Computer Vision
2 Select annotation method
Keypoints
3 Specify how many objects to label
Amount
objects
SmallMediumLarge
4 Check the approximate total cost
Estimated Cost:$0 /total
5 Run free pilot
Why Projects Choose Engai for Video Annotation
97%
Tracking Accuracy
8x
Faster Delivery
30%
Cost Reduction
Temporal Consistency
We maintain object identity across thousands of frames, ensuring your tracking models learn from coherent, continuous sequences.
Frame-Level QA
Every annotation file is validated for ID consistency, label accuracy, and geometric precision before delivery.
Scalable Video Volume
From short clips to multi-hour sequences, we scale annotation throughput dynamically to match your project timeline.
Safe and secure
Your trust is our foundation. Engai is designed with a deep commitment to data privacy and security. Visit our trust page and security center to learn more.
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Discover how Engai's Data Pipeline and AI Infrastructure platform can make your organization AI-ready:
Automatically discover and map all datasets with contextualized inventory
Effortlessly manage ML lifecycle and address governance gaps
Drastically reduce deployment time by mitigating edge-case risks
Immediately detect and respond to model drift to minimize threat impact
Proactively apply zero-trust protection mechanisms for proprietary data
Live Infrastructure Telemetry
1.2BData Points
189Models
24Pipelines
3Alerts
TypeOriginOwnerStatus
Vision ModelGitHubDave DigsterHealthy
DatasetAWS S3Emily ThompsonSyncing
DeploymentGCPRobert BrownActive
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.
Support Center
Frequently Asked Questions
Video annotation requires maintaining object identity (tracking IDs) consistently across hundreds or thousands of frames, including through occlusions, lighting changes, and camera motion. It also involves temporal labeling — marking when actions start and end — which requires annotators who understand motion patterns and object behavior over time, not just static geometry.