This blog examines video annotation for Generative AI and outlines core challenges, explores modern annotation, highlights practical use cases...
Read MoreVideo Annotation Services for Computer Vision
Digital Divide Data delivers scalable video annotation services to train computer vision models to detect, track, and interpret objects and events across video frames.
Turning Visual Motion into AI Intelligence with Video Annotation Services
Digital Divide Data (DDD) is a global provider of video annotation and computer vision data services. We combine expert human annotation, advanced quality frameworks, and secure infrastructure to help organizations transform raw video data into high-quality training datasets for AI and machine learning applications.
Video Annotation Workflow End-to-End
Fully Managed Video Annotation, From Raw Footage to Model-Ready Data
Use Case & Annotation Strategy Definition
Align on objectives, classes, events, frame rates, and quality metrics.
Secure Video Ingestion & Preparation
Videos are securely ingested, segmented, and optimized for annotation.
Frame-Level & Temporal Annotation
Expert annotators label objects, events, and motion across video sequences.
Multi-Layer Quality Assurance
Automated checks, reviewer validation, and temporal consistency audits.
Edge Case & Bias Review
Identification of rare events and balanced representation across scenarios.
Types of Video Annotation We Support
2D Bounding Box Annotation
Rectangular boxes are drawn around objects in each frame to identify, locate, and track moving entities across videos.
3D Cuboid Annotation
Objects are annotated in three dimensions to provide spatial depth and orientation for applications like autonomous driving.
3D Point Cloud Annotation
We annotate 3D data points in video sequences to support scene understanding, motion analysis, and spatial tracking.
Landmark Annotation
Key landmarks such as buildings, roads, or natural features are labeled, including movement direction and speed when required.
Lines & Splines Annotation
Curves, paths, and linear features are annotated to track trajectories, boundaries, and motion patterns over time.
Polygon Annotation
Complex object boundaries are outlined using polygons for precise tracking and segmentation in dynamic scenes.
Event Classification
Video clips are labeled based on specific actions or events, supporting activity recognition and behavioral modeling.
Event Tracking
Annotated datasets enable AI systems to track recurring events and analyze movement patterns and behavioral frequency.
Video Annotation Use Cases
Industries We Support
Autonomous Vehicles
Training video perception models to detect, track, and predict real-world driving scenarios.
Robotics
Enabling robots to interpret dynamic environments and human interactions through video.
Humanoids
Powering human-like perception with motion, gesture, and activity understanding.
AgTech
Monitoring crop growth, equipment movement, and environmental changes via video data.
Geospatial Intelligence
Annotating aerial and satellite video for mapping, monitoring, and situational awareness.
Retail & E-Commerce
Analyzing customer movement, engagement, and in-store behavior patterns.
What Our Clients Say
Why Choose DDD?
Expert Human-in-the-Loop Annotation
Our trained annotators ensure temporal accuracy, contextual understanding, and precise handling of complex and edge-case video scenarios.
Scalable Video Annotation Services
We scale seamlessly from short clips to massive video archives, supporting evolving AI and production requirements.
Multi-Modal Expertise
Comprehensive support for 2D video, 3D cuboids, point cloud data, and multisensor-fused video datasets.
Faster Model Iteration Cycles
Optimized pipelines and automation reduce turnaround times for large, video-intensive datasets.
DDD’s Commitment to Security & Compliance
Your computer vision datasets are protected at every stage through globally recognized security standards and secure operational infrastructure.
SOC 2 Type 2
Verified controls across security, confidentiality, and system reliability
ISO 27001
Holistic information security management with continuous audits
GDPR & HIPAA Compliance
Responsible handling of personal and sensitive data
TISAX Alignment
Automotive-grade protection for mobility and vehicle-AI workflows
Read Our Latest Blogs
Explore expert perspectives on multisensor fusion, perception architectures, and emerging trends
Video Annotation for Autonomous Driving: Key Techniques and Benefits
In this blog, we explore important aspects of video annotation for autonomous driving, its various techniques, and how it’s...
Read MoreTrain AI to Understand Motion with High-Quality Video Annotation Services
Frequently Asked Questions
What are video annotation services?
Video annotation services involve labeling and tagging objects, events, and activities across video frames to train computer vision and machine learning models to understand motion, behavior, and temporal context.
How is video annotation different from image annotation?
Video annotation requires labeling objects consistently across multiple frames, enabling object tracking, motion analysis, and event detection, making it more complex than single-image annotation.
What types of video annotation does DDD provide?
DDD supports frame-by-frame annotation, video classification, event-based timestamp labeling, live video stream monitoring, video moderation, and event tracking.
Which annotation techniques are used in video annotation?
We use 2D bounding boxes, 3D cuboids, polygons, point cloud annotation, landmarks, lines and splines, and event-based labels depending on the use case.
Can DDD annotate long or high-frame-rate videos?
Yes. Our scalable video annotation services support short clips, long-duration videos, and high-frame-rate footage across large datasets.
Can DDD help reduce bias in video training datasets?
Yes. We apply bias-aware sampling, balanced labeling, and diverse data coverage to improve fairness and real-world reliability.