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Video Annotation and Labeling Services

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Unidata provides comprehensive video annotation services to support AI video analytics across 20+ industries. Our team ensures precise, efficient annotations, helping organizations extract valuable insights from their video data.

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95%+ annotation accuracy
1,000+ domain-matched annotators
Pilot launched within days

Industries

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Autonomous Vehicle

Dynamic road understanding, motion tracking, and real-time decision-making for safe driving.

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Retail & E-commerce

Consumer behavior analysis, shopping trends, and inventory tracking for optimal operationsю

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Agriculture

Crop monitoring, pest detection, and livestock tracking through drone and field camera footage.

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Healthcare

Surgical procedure analysis, patient monitoring, and recovery tracking for better care.

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Finance

Transaction review, fraud detection, and customer interaction analysis for security.

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Security & Surveillance

Threat identification, movement tracking, and real-time alerts across camera networks.

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Manufacturing

Production line monitoring, defect detection, and predictive maintenance automation.

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Customer Service & Support

Emotion recognition, issue tracking, and response optimization for better satisfaction.

Data Annotation Vs Labeling Tasks

Video Data AnnotationVideo Data Labeling
DefinitionFrame-by-frame or sequence-level detailed marking with temporal consistency for moving objects and eventsAssigning classification labels to entire videos or simple time-based scene tags
Work CoverageTemporal understanding: object tracking, action sequences, event boundaries, and motion patternsVideo-level or clip-level categorization without frame-level spatial details
Common Tasks
  • Object tracking across frames
  • Temporal action localization
  • Activity recognition marking
  • Event boundary detection
  • Multi-object interaction labeling
  • Pose estimation across sequences
  • Video-level classification
  • Scene change detection
  • Simple object presence tagging
  • Clip-level timestamp labeling
  • Content categorization
Complexity LevelVery high complexity: requires temporal reasoning, object persistence understanding, and motion pattern analysisLow to medium complexity: primarily descriptive tagging without spatial or temporal precision
ML ImpactEnables: action recognition, object tracking, temporal localization, event detection, activity predictionEnables: video classification, scene recognition, content filtering, basic video search

Image Data Annotation Types

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Object Tracking

Bounding boxes and polygons applied by expert annotators to identify and track objects across video footage frames, enabling motion tracking, trajectory prediction, and object detection for computer vision and surveillance ML models.
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Semantic Segmentation

Pixel-level labels assigned to every frame in raw video by human annotators, giving learning models the dense scene understanding needed for autonomous driving, security systems, and AI-powered visual data analysis.
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Instance Segmentation

Per-pixel masks distinguishing individual object instances of the same class across video footage, enabling crowd analysis, defect detection, and multi-agent scene parsing for properly annotated training datasets.
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Action Recognition

Frame-sequence labels identifying human actions, gestures, and activities across video clips, providing high-quality annotated datasets for activity detection, motion analysis, and behavior recognition in AI models.
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Keypoint Annotation

Facial landmarks, joint positions, and object corners marked and tracked by expert annotators across video segments, giving pose estimation and face recognition models the spatial training data for gesture and motion analysis.
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Event Tracking

Temporal tags and event labels tied to specific in-frame triggers across video footage, structuring high-quality datasets for predictive ML algorithms and automating video intelligence workflows.
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Temporal Segmentation

Scene-change boundaries and activity intervals labeled across raw video files, enabling clip classification, metadata extraction, and contextual scene understanding for AI-powered video search and recommendation models.
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Polyline Annotation

Vector path lines drawn over video frames to label roads, lanes, and object movement paths, giving autonomous driving and robotics models the directional training data for lane detection and motion tracking.
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3D Cuboid Annotation

Three-dimensional bounding boxes and 3D cuboids capturing object depth, height, and spatial orientation across video frames, providing volumetric annotated datasets for autonomous vehicles, robotics, and AR model training.
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Object Detection

Per-frame localization labels identifying and tagging objects in video files without cross-frame continuity, giving computer vision models the accurately annotated ground truth for security cameras, retail analytics, and industrial inspection.
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Scene Text Recognition

Text regions and video transcription labels extracted from in-frame signage, documents, and overlays by expert annotators, enabling ML algorithms to detect, parse, and digitize visual text content across video footage.

The best software for video annotation tasks

V7

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V7 is a powerful video annotation platform designed for handling complex and large-scale annotation tasks. It offers advanced tools for annotating videos, including object tracking and instance segmentation, with AI-assisted automation to enhance efficiency.

Best For:

Teams and enterprises looking for a comprehensive, AI-driven video annotation platform that supports advanced features and large datasets.

Key Features

  • AI-powered auto-annotation and object tracking.
  • Supports a wide range of annotation types, including 3D cuboids, polylines, and semantic segmentation.
  • Collaboration tools for managing large teams and complex projects.
  • Real-time quality control and workflow automation.

SuperAnnotate

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SuperAnnotate is an advanced platform that excels in both image and video annotation tasks. It offers high precision and automation, making it ideal for projects requiring detailed and accurate annotations across multiple video frames.

Best For:

Teams looking for a powerful, AI-assisted annotation tool that can handle complex video data with precision.

Key Features

  • Automated annotation tools powered by AI to speed up the annotation process.
  • Collaboration tools for large teams, with role-based access control.
  • Supports a wide range of annotation types, including bounding boxes, polygons, and keypoints.
  • Integration with popular machine learning frameworks and cloud storage.

CVAT (Computer Vision Annotation Tool)

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CVAT is an open-source tool developed by Intel, specifically designed for video and image annotation. It is highly customizable and supports a wide range of annotation types, making it suitable for complex projects that require flexibility.

Best For:

Developers and researchers who need a free, customizable tool for detailed and complex video annotation tasks.

Key Features

  • Free and open-source with a strong community for support.
  • Supports various video annotation tasks, including object tracking and action recognition.
  • Advanced tools for manual and semi-automated annotation.
  • Highly customizable with support for custom scripts and plugins.

Labelbox

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Labelbox is a versatile annotation platform that extends its capabilities to video annotation. It offers robust tools for managing large-scale projects, with AI-assisted features that help streamline the annotation process.

Best For:

Enterprises and teams needing a scalable video annotation solution with strong project management and collaboration capabilities.

Key Features

  • AI-powered tools for automating repetitive tasks, including object tracking.
  • Supports a variety of video annotation types, including frame-by-frame labeling and semantic segmentation.
  • Integrated project management and collaboration features.
  • API support for seamless integration with machine learning workflows.

Scalabel

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Scalabel is an open-source platform designed for scalable video and image annotation. It supports a wide range of annotation tasks and is particularly strong in managing large datasets and collaborative projects.

Best For:

Teams and organizations looking for an open-source, scalable solution for large-scale video annotation projects.

Key Features

  • Supports multiple video annotation types, including object tracking, 3D bounding boxes, and semantic segmentation.
  • Real-time collaboration features for team-based projects.
  • Scalable architecture suitable for large datasets.
  • Open-source and customizable to fit specific project needs.

VoTT (Visual Object Tagging Tool)

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VoTT is an open-source annotation tool by Microsoft that offers a simple and flexible solution for video annotation tasks. It supports various output formats and integrates well with cloud services like Azure.

Best For:

Teams and developers needing a straightforward, Azure-integrated tool for video annotation tasks.

Key Features

  • User-friendly interface for creating and managing video annotations, including object tracking.
  • Supports export in popular formats like YOLO, TFRecord, and CSV.
  • Integration with Azure ML and other cloud-based services.
  • Free and open-source with active development and support.

Datasaur

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Datasaur is a robust annotation platform that supports both text and video annotation. It is designed for teams that require real-time collaboration and high-quality annotations, with features tailored for detailed video analysis.

Best For:

Teams that require a collaborative environment with advanced video annotation capabilities and robust quality control features.

Key Features

  • Real-time collaboration tools for team-based annotation.
  • Supports complex video annotation tasks, including keypoint annotation and event tracking.
  • AI-powered suggestions to improve speed and accuracy.
  • Detailed analytics and reporting to track project progress.

RectLabel

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RectLabel is a macOS-based video annotation tool focused on simplicity and efficiency. It is particularly useful for users within the Apple ecosystem who need to perform quick and straightforward video annotations.

Best For:

MacOS users looking for a simple and effective tool for video annotation tasks, particularly for quick and easy project execution.

Key Features

  • Intuitive macOS interface with support for video annotation tasks, including bounding boxes and polylines.
  • Customizable shortcuts and tools for efficient annotation.
  • Supports exporting in various formats, including YOLO, COCO, and VOC.
  • Lightweight and optimized for quick use on Mac devices.

How Unidata Provide Data Labelling Process

A Clear, Controlled Workflow From Brief to Delivery

01 Kickoff Briefing and Task Setup
You
Share your raw data, annotation requirements, and quality standards
Unidata
We analyze your data, define the methodology, and assign a dedicated project lead. The right annotation type and domain-matched annotators are confirmed before anything starts.
02 Pilot & Scoping Pilot and Estimate
You
Review annotated samples, validate quality, and approve scope before full-scale work begins.
Unidata
We annotate a small representative sample and deliver a clear cost estimate broken down by complexity, hours, and validation rounds.
03 Legal & Confidential Agreement and NDA
You
Review and sign. Scope, quality thresholds, and deadlines are all defined in writing upfront.
Unidata
We prepare a full confidentiality agreement covering your data, guidelines, and any proprietary model details.
04 Technical Setup Tools and Workflow Configuration
You
Share existing guidelines and format requirements. No guidelines yet? We build them together.
Unidata
We configure the right annotation platform for your data type: Labelbox, SuperAnnotate, CVAT, or Label Studio. Workflows, label taxonomy, and quality benchmarks are set before a single label is applied.
05 Execution Annotation in Progress
You
Review sample batches at each milestone and share feedback with your project lead.
Unidata
Trained, domain-matched annotators work through your dataset. No batch moves forward without passing internal quality checks.
06 QA Human-in-the-Loop Review
You
Review edge cases and confirm acceptance criteria before final delivery.
Unidata
Every batch goes through automated validation and human review. Inter-annotator agreement (IAA) is tracked throughout. Inconsistencies are caught and resolved before the dataset moves forward.
07 Delivery Production-Ready Dataset
You
Receive your annotated dataset in the format you need: COCO, Pascal VOC, JSON, CoNLL, PCD, or custom. Full quality report included.
Unidata
Clean, validated, training-ready data delivered on schedule. Final invoice aligned to the scope agreed at Step 02.

Have questions about the process? Every project starts with a free consultation — no commitment required.

Request Custom Research

Data Annotation Challenges? Value You Get with Unidata

Real Challenges

  • No annotators, tools, or workflow to process collected data
  • No quality check on labeled data before it hits the pipeline
  • No way to ensure two annotators label the same object consistently
  • Can’t find annotators with LiDAR, medical, or financial expertise
  • Scope creep and rework cycles exhaust the budget before delivery

Value with Unidata

  • Project lead assigned and pilot launched within days
  • Every batch validated before delivery, 95%+ accuracy via multi-stage QA
  • Label consistency tracked per batch, issues caught before training fails
  • 1,000+ annotators matched by domain — the right expert, every time
  • Pilot-first pricing, fixed scope, zero hidden rework charges

Data Annotation Files Example

Working with annotation data from CVAT and JSON formats, you'll receive optimized code that seamlessly processes both file types, complete with practical examples and visual representations of your data structure.

Other Services

Ready-Made Datasets

Get our ready-made datasets to enhance the quality of your models and improve testing

Data Collection

Collect and enhance diverse image, video, text, and audio data for your business

Data Annotation

Get accurate data labeling and annotation for your machine learning projects

LLM Training Services

Comprehensive data services for training, evaluation, and testing of LLM models across 12 industries

What our clients are saying

UniData

4 3 Reviews

PA

Paul 2025-02-21

Very Positive Experience!

The team was very responsive when requesting a specific dataset, and was able to work with us on what data we specifically needed and custom pricing for our use case. Overall a great experience, and would recommend them to others!

TH

Thorsten 2025-01-09

Very good experience

We got in touch with UniData to buy several datasets from them. Communication was very cooperative, quick, and friendly. We were able to find contract conditions that suited both parties well. I also appreciate the team's dedication to understand and address the needs of the customer. And the datasets we bought from UniData matched with our expectations.

Max Crous 2024-10-08

Data purchase

Our team got in touch with UniData for purchasing video data. The team at UniData was transparent, timely, and pleasant to communicate and negotiate with. Their samples and descriptions aligned well with the data we received. We will certainly reach out to UniData again if we're in search of 3rd party video data.

Abhijeet Zilpelwar 2025-02-26

Data is well organized and easy to…

Data is well organized and easy to consume. We could download and use it for training within few hours of receiving the data links.

Trusted by the world's biggest brands

FAQ

What is Video Annotation?
Video annotation for machine learning (ML) is the process of labeling objects, actions, and events within video footage to create structured datasets for AI model training. It involves analyzing sequences of frames so ML algorithms can understand motion, context, and interactions over time.
By accurately annotating elements such as objects, movements, and temporal changes, video annotation enables AI systems to perform tasks like object tracking, action recognition, motion analysis, and behavior detection more effectively.
Why is video annotation important for Artificial Intelligence and Machine Learning?
Video annotations provide high-quality training data required for vision models and ML algorithms. Accurate annotated datasets help AI models understand movement, context, and interactions across video segments, improving real-world performance.
What types of video annotation do you support?
We support a wide range of annotation types, including object tracking, semantic segmentation, keypoint annotation, action recognition, LiDAR annotation and 3D cuboids. These techniques enable precise object detection, motion tracking, and analysis of complex visual data.
What are the risks of poor-quality video annotation?
Low-quality video annotations can compromise the entire training process, leading to inaccurate predictions and weaker performance of computer vision and AI models. Inconsistent labeling across video frames creates confusion for ML algorithms, resulting in higher retraining costs, project delays, and unreliable results in tasks like object tracking and motion analysis.
What is the minimum dataset size required for video data annotation services?
We typically work with datasets starting from 500–5,000 data points (video clips or segments), while 5,000–50,000 is a common range for building high-quality training datasets. For pilot projects, we usually annotate 10–100 video samples, depending on the complexity of the task and annotation techniques required.
Can I order a pilot project?
Yes, Unidata offers pilot projects so your ML teams can evaluate video annotation quality, workflows, and compatibility with their ML models. This helps validate outsourcing decisions before scaling to full training datasets.
How is my data kept secure?
All services are GDPR and CCPA compliant, running on AWS infrastructure certified under ISO 27001 and ISO 27701. Strict access controls are applied during the annotation process.
How do you ensure the quality of video annotations? Do you use automation for validation?
We combine human expertise with a structured validation workflow to ensure high-quality video annotations. Each project undergoes multiple review stages, from initial checks to final validation, to maintain consistency of video frames and segments. We monitor key metrics such as Error Rate, IAA (Inter-Annotator Agreement), and IoU (Intersection over Union), and use benchmark (“golden”) samples to continuously evaluate performance, supported by AI-assisted tools.
How long does it take to complete a video annotation project?
Timelines depend on video length, dataset size, and annotation complexity, so there is no fixed estimate. We evaluate each project individually and provide a clear delivery timeline based on your requirements.
What technical support do you provide after purchasing data annotation services?
Clients get continuous support from our project managers helping clients with any questions during video data annotation process.

Why Companies Trust Unidata’s Services for ML/AI

Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.

Rely on 1,100+ Experts

  • 1,100+ in-house labelers and specialists
  • Consistent quality and rapid scaling
  • Complex multi-type annotation projects
01

Discover 19+ Industry Expertise

  • Finance, IT, E-commerce, Retail, Healthcare, Medical, Fintech, and more
  • Deep domain knowledge for industry-specific requirements
  • Support for industry-specific annotation challenges
02

Get Turnkey Services for ML/AI

  • From data collection to labeling and validation
  • Project tailored to your requirements
  • Complex annotation, multiple annotation types at once
03

Ensure Legal & Secure Data

  • GDPR & CCPA compliant
  • AWS ISO 27001/27701 storage
  • Curated and legally sourced
04

Process Different Content Types

  • Multimodal Data: 333K+ texts, 550K+ audio, 11K+ videos, 26K+ images
  • Formats: DICOM, LiDAR, and specialized types
  • Annotation: multiple types at once with high accuracy
05

Request Custom Research

Have questions about the process? Every project starts with a free consultation — no commitment required.

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