10 Best Video Annotation & Labeling Tools In 2024

Sumit Singh -

Videos are quickly becoming a vital tool for businesses to help them maximize visibility, attract more customers and build loyalty.

In 2023, 91% of businesses were using video as a marketing tool.

Even today, videos are dominating the marketing industry in every format imaginable.

Though videos are the most powerful and engaging communication medium in modern times, managing and analyzing them can be a huge challenge.

And, this is where video annotation tools can help you out.

Whether you're a brand trying to improve your promotional videos, an educator wanting to create more engaging lessons, or a content creator sharpening your storytelling, video annotation is the solution for you.

Video annotation is an essential task in machine learning, computer vision, and natural language processing. It involves the process of labeling and tagging various elements in a video to help machines learn and understand it better.

If you want to create high-quality videos, this blog will help you find the perfect video annotation software.

Our video annoation AI experts have carefully curated this list based on factors such as user-friendliness, accuracy, speed, and cost-effectiveness.

Let's dive in!

Table of Contents

  1. Labellerr
  2. VGG Image Annotator (VIA)
  3. Supervise.ly
  4. Labelbox
  5. CVAT
  6. Dataloop
  7. Annotorious
  8. RectLabel
  9. Hasty.ai
  10. Amazon SageMaker Ground Truth
  11. Conclusion
  12. FAQ

Top 10 Video Annotation Tools

Here are details on the top 10 video annotation software that are currently available in the market that you can consider for your model training:

1. Labellerr

Labellerr is a cloud-based video annotation tool that offers an easy-to-use interface and supports various video formats.

It is one of the unique tools that support automated data labeling features that can help you speed up your model training process.

It provides a range of annotation tools, such as

  • Bounding boxes
  • Polygons
  • Key points
  • and semantic segmentation

With Labellerr, you can annotate videos with high accuracy and speed, and also collaborate with team members in real-time.

Features

  • Semantic segmentation
  • Instance segmentation
  • Bounding box annotation
  • Polygon annotation
  • Skeletal annotation
  • Key points annotation
  • Bitmask annotation
  • Custom annotation
  • Lane annotation

Pros:

  • Feature-rich Video Segmentation: Perform faster segmentation with pixel perfection. Drag polygon and auto-bordering features prevent overlapping adjacent objects in videos.
  • Auto-labeling: Accelerate use cases with semantic segmentation using features like SAM and active learning, now applied to video data.
  • Professional Annotation Team: Handle large volumes of video data with fast turnaround times.
  • Custom SLA: Starting from 24 hours for batch completion of video annotations.
  • 24/7 Tool Support: Available for the Enterprise Plan, ensuring uninterrupted video annotation operations.
  • Robust QA Process: Set up QA processes that include agreement between annotators, comparison based on ground truth and IOU metrics, model-assisted QA, generative AI-powered QA, and sample visual quality assurance, now tailored for video annotation.
  • Dedicated Account Manager: Manage daily/weekly video annotation output efficiently.
  • Data Privacy and Security Compliance: Comply with HIPAA and GDPR regulations for video data privacy and security.
  • Multi-tier Pricing: Options based on quality measurement for video annotation. The default QC process includes 1 round of annotation and 1 round of QC, customizable to match expected video annotation output, timeline, and budget.

Cons:

  • Limited Format Support: This does not currently support point cloud and 3D video data formats.

Pricing:

  • Pro Plan: Starts at $499 per month for 10-user access with 50,000 data credits included. Additional data credits can be purchased at $0.01 USD per data credit, and extra users can be subscribed to at $29 USD per user.
  • Enterprise Plan: Offers professional services, including tool customization and ML consultancy tailored for video annotation needs.

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G2 Review : 4.8/5 (link to customer reviews)

2. VGG Image Annotator (VIA)

VIA is a free, open-source tool developed by the Visual Geometry Group at the University of Oxford.

It supports image and video annotation for various tasks, including object detection and segmentation. Users can annotate directly in their browsers, making it accessible and easy to use.

Features:

  • Supports multiple annotation types (bounding boxes, polygons).
  • Browser-based with no installation required.
  • Export options for different formats (JSON, CSV).

Pros:

  • Free and open-source.
  • User-friendly interface.
  • Lightweight with no server dependencies.

Cons:

  • Limited collaborative features.
  • No built-in machine learning capabilities.

Pricing:
Free.

3. Supervise.ly

Supervise.ly is a cloud-based annotation tool that supports video annotation as well as image annotation.

It offers a wide range of annotation tools such as bounding boxes, polygons, and semantic segmentation.

It also provides automation features to improve annotation speed and accuracy.

Features:

  • Multi-format support (images, videos, 3D).
  • AI-assisted annotations and auto-labeling.
  • Project management and team collaboration tools.

Pros:

  • Versatile annotation capabilities.
  • Intuitive interface.
  • Strong community support.

Cons:

  • Can be overwhelming for beginners.
  • Higher costs for advanced features.

Pricing:
Starts at $25/month/user (basic plan).

G2 Review : 4.7/5 (link to customer reviews)

4. Labelbox

Labelbox is a popular data-labeling platform that offers a user-friendly interface for annotating images and videos.

It’s designed for scalability, making it suitable for teams of all sizes. Labelbox also integrates with machine learning workflows to streamline the data preparation process.

Features:

  • Supports various annotation types (bounding boxes, segmentation).
  • Collaborative tools for team projects.
  • API access for integration with ML pipelines.

Pros:

  • Highly customizable workflows.
  • Strong collaboration features.
  • AI integration enhances efficiency.

Cons:

  • Can become costly as you scale.
  • Learning curve for new users.

Pricing:
Custom pricing based on usage.

G2 Review : 4.7/5 (link to customer reviews)

5. CVAT

Computer Vision Annotation Tool (CVAT) is an open-source tool developed by Intel.

It’s specifically designed for annotating video and image data, offering a range of features that cater to both novice and experienced annotators in AI projects.

Features:

  • Supports multiple annotation formats.
  • Customizable user interface.
  • Automatic annotation using pre-trained models.

Pros:

  • Open-source and free to use.
  • Strong community and documentation.
  • Flexible and powerful for advanced users.

Cons:

  • Requires installation and server setup.
  • User interface can be complex for beginners.

Pricing:
Free

G2 Review : 4.5/5 (link to customer reviews)

6. Dataloop

Dataloop is a cloud-based video annotation tool that supports a range of annotation types such as bounding boxes, polygons, and semantic segmentation.

It also offers automation features such as pre-built models and workflows, as well as real-time collaboration for team members.

Features:

  • Supports video, images, and audio annotations.
  • Advanced quality control tools.
  • Built-in machine learning capabilities for faster labeling.

Pros:

  • Comprehensive data management features.
  • Scalable for large teams.
  • Intuitive dashboard for project oversight.

Cons:

  • Can be expensive for small teams.
  • Some features may be underutilized.

Pricing:
Custom pricing based on project requirements.

G2 Review : 4.5/5 (link to customer reviews)

7. Annotorious

Annotorious is a free and open-source image and video annotation tool that offers a simple and user-friendly interface.

It supports various annotation types such as bounding boxes and points and also provides real-time collaboration features.

Features:

  • Easy-to-use interface for quick annotations.
  • Supports various annotation types (rectangles, polygons).
  • Embeddable in websites and applications.

Pros:

  • Free and easy to set up.
  • Lightweight and responsive.
  • Good for small projects.

Cons:

  • Limited advanced features.
  • Not as suitable for large-scale projects.

Pricing:
Free.

8. RectLabel

Overview:
RectLabel is a macOS application designed for image and video annotation, particularly for machine learning projects.

It allows users to create high-quality datasets for training AI models with various annotation options.

Features:

  • Supports bounding boxes and polygon annotations.
  • Built-in export options for TensorFlow and other formats.
  • Simple interface for quick labeling.

Pros:

  • Affordable one-time purchase.
  • Offline functionality for macOS users.
  • Quick setup and ease of use.

Cons:

  • Limited to macOS.
  • Fewer collaboration features compared to cloud solutions.

Pricing:
$29.99 (one-time purchase).

9. Hasty.ai

Hasty.ai is a cloud-based annotation tool that supports video annotation as well as image annotation.

It offers a range of annotation types such as bounding boxes, polygons, and semantic segmentation, and also provides automation features such as pre-built models and workflows.

Features:

  • AI-assisted labeling to speed up the process.
  • Collaboration tools for teams.
  • Comprehensive annotation types, including segmentation.

Pros:

  • Fast and efficient due to AI integration.
  • User-friendly interface.
  • Supports team collaboration.

Cons:

  • Subscription pricing can add up.
  • May require some learning for optimal use.

Pricing:
Starts at $49/month/user.

G2 Review : 4.4/5 (link to customer reviews)

10. Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth is a cloud-based annotation tool that supports various annotation types such as bounding boxes, polygons, and lines.

It also offers automation features such as pre-built models and workflows, as well as integration with other AWS services.

Features:

  • Automated labeling with built-in ML models.
  • Supports multiple data formats (images, videos, text).
  • Integration with other AWS services.

Pros:

  • Scalable and highly efficient.
  • Access to AWS's powerful infrastructure.
  • Comprehensive support for various use cases.

Cons:

  • Complex pricing structure.
  • Requires familiarity with AWS.

Pricing:
Pay-as-you-go pricing; $0.50 per labeled image (variable).

G2 Review : 4.1/5 (link to customer reviews)

Conclusion

As video continues to dominate marketing and communication, the ability to accurately label and tag video data is critical for success. Without the right video annotation tools, the process can be tedious and time-consuming, slowing down your projects.

In this guide, we’ve explored the top 10 video annotation tools that can help you automate, streamline, and improve the accuracy of your work.

Among all solutions, Labellerr stands out as the leading video annotation software because it’s designed to help you label video data 10X faster with incredible precision and ease.

Whether you're building AI models for computer vision, machine learning, or deep learning, Labellerr provides an advanced, feature-rich platform that can handle the most complex tasks in video annotation.

Want to elevate your video projects?

Choose the right annotation tool and start boosting productivity, improving accuracy, and speeding up your workflow.

Let us know how we can help you. Schedule a call today.

Read our other listicles:
1. Top 10 Auto Annotation & Labeling Tools in 2024

2. Top 10 Best Image Labeling & Annotation Tools in 2024

Frequently Asked Questions

1.What is video labeling?

Video labeling is tagging objects in video files for computer vision AI models to detect certain classes, for example, persons, vehicles, animals, etc.

2. What is the best tool to label images?

There are various open-source and paid tools available with pros and cons. It depends on the use cases and which tools fit best for the use cases. Some notable names are Labellerr, CVAT, V7, and Roboflow.

3. What is an example of video annotation?

There are many examples, the most popular is self-driving cars where video footage gets labeled to detect pedestrians, vehicles, road lanes, etc.