10 Best Video Labeling Tools

Video content has emerged as the backbone for revolutionizing industries like self-driving vehicles, security, medicine, and AR. These industries mostly depend on machine learning models that need suitably annotated videos so that they can understand what they see and act in the physical world.

Video labeling tools play a crucial role in creating these datasets by offering advanced features to annotate objects, track movements, and label actions across thousands of frames. However, various video labeling software are available in the market, and you should carefully select one based on your requirements. Your decision will impact your dataset’s data preparation speed, manual effort, consistency, and accuracy.

This article explores the 10 best video labeling tools available today, each designed to handle the unique challenges of video annotation. No matter if you have a large-scale project or just want to reduce the amount of manual labor, this roundup will give you the most intuitive video annotator for your use case.

Video labeling is the process of identifying and tagging specific objects, actions, or frames in video content to prepare it for machine learning and computer vision applications. This process is essential in fields like autonomous driving, surveillance, sports analytics, robotics and augmented reality to create labeled datasets that help ML models recognize patterns, detect objects, and understand scenes.

However, video labeling is complex and time-consuming due to the sheer volume of frames and the need for precise annotations. When video data is labeled manually, it involves a lot of work, and it is prone to errors and inconsistencies, especially if the video has many frames or is quite prolonged.

This is where video annotation tools come in. Video labeling significantly improves the accuracy and the speed of video labeling by providing features like:

• Frame-by-Frame and Continuous Tracking: Allows individual labeling of each frame or tracking of objects across multiple frames.
• Automated Object Detection and Tracking: AI-driven object tracking identifies and follows objects across frames, significantly reducing manual intervention. This is especially useful for labeling moving objects, such as cars or people, in lengthy video sequences.
• Event and Action Recognition: Many tools support action and event tagging (e.g., running, walking), which helps classify activities efficiently. This is critical for applications in sports analytics or security.
• Semantic and Instance Segmentation: These tools allow for pixel-level annotations, categorizing each part of a frame into specific classes or instances. This is essential for fields like autonomous driving, which require detailed and precise labeling.
• Training and Validation Data Management: Many labeling tools help organize labeled data in machine learning workflows, splitting datasets into training and validation sets.

Video labeling is essential because it provides annotations to the raw video footage, enabling machine learning models to see and interpret visual information accurately. Without labeled data, AI models lack the context needed to recognize objects, track movements, and understand actions in real-world environments.

To understand the full impact of video labeling for machine learning, let’s look at how it enables AI models to perform accurately and reliably across various fields.

1. Real-Time Recognition and Decision-Making

For applications like autonomous driving, labeled video data allows AI to identify objects (e.g., vehicles, pedestrians) and track movements accurately. Techniques such as bounding boxes and 3D cuboids help detect and measure distances, while optical flow tracking captures motion across frames.

2. Action Recognition in Healthcare and Security

In healthcare, video labeling supports models that track body movements or identify medical symptoms. Keypoint and skeleton annotations label points on a person’s body (e.g., joints), useful in analyzing movements for physical therapy. In security, labeling actions (e.g., walking, running) helps detect behavior patterns.

3. Reducing Manual Effort with Automation

AI-assisted tools help reduce manual work in labeling by using automated tracking to pre-label frames. This is particularly helpful when working with large datasets where maintaining quality and uniformity is challenging.

4. Supporting Innovation

For robotics and AR/VR, advanced techniques like 3D annotations and semantic segmentation allow models to understand depth and differentiate between complex objects. This is vital for robots that need spatial awareness or AR applications that interact with real-world objects.

5. Improving Model Reliability through Detailed Labeling

In fields like security and robotics, precise labeling reduces errors and improves model reliability. Instance segmentation distinguishes individual instances of objects (e.g., multiple people in a frame), which is important for identifying specific targets.

1. DagsHub

DagsHub offers a comprehensive video labeling solution through Label Studio, an open-source data labeling tool. This integration provides a fully configured labeling workspace within each DagsHub repository, enabling users to annotate video data directly from the platform.

Main Features:

• Diverse Annotation Tools: Supports various annotation types, including bounding boxes, polygons, keypoints, and semantic segmentation, facilitating detailed video labeling.
• Automated Labeling: Utilizes machine learning models to predict labels, reducing manual effort and enhancing efficiency. This is achieved through Label Studio’s ML Backend, which can be configured to work with models registered on DagsHub via MLflow.
• Version Control: Annotations are fully versioned, allowing users to track changes, compare different annotation versions, and revert to previous states if necessary.
• Collaboration: Facilitates team collaboration by enabling multiple users to work on the same project, with features for reviewing and validating annotations collectively.
• Data Management: Integrates seamlessly with DagsHub’s data management features, allowing users to organize, visualize, and manage video datasets effectively.

Pricing: Offers a free tier with basic features; premium plans are available for advanced functionalities.

2. Labellerr

Labellerr is a cutting-edge video annotation tool that leverages automation and advanced annotation techniques to deliver precise datasets efficiently. It is particularly suited for projects requiring high-quality annotations in domains like healthcare, autonomous systems, and surveillance.

Main Features:

• Advanced Annotation Types: Offers bounding boxes, polygons, semantic segmentation, skeletal annotations, and custom annotations to address diverse video labeling needs.
• AI-Powered Auto-Labeling: Uses machine learning models for pre-labeling, reducing manual work and speeding up video annotation workflows.
• Pixel-Perfect Segmentation: Includes features like drag-polygon and auto-bordering to eliminate overlaps and improve annotation accuracy for dense scenes.

Pricing: Free plan for researchers, Pro plan starting at $499/month and Customized pricing based on request.

3. SuperAnnotate

SuperAnnotate is a versatile data annotation platform designed to streamline video labeling with its powerful combination of manual and automated tools. Its robust project management features and collaborative workflows make it a top choice for teams working on large-scale, high-quality video datasets.

Main Features:

• Advanced Annotation Options: Supports a wide range of video labeling needs, including bounding boxes, polygons, key points, instance segmentation, and semantic segmentation, ensuring comprehensive and precise annotations.
• AI-Assisted Video Annotation: Automates repetitive tasks like tracking objects across frames, accelerating workflows, and reducing human error.
• Quality Control for Video Datasets: Offers features like consensus review, inter-annotator agreement analysis, and built-in metrics calculation to maintain accuracy and consistency across annotations.

Pricing: Offers a free trial; pricing details are available upon request.

4. Labelbox

Labelbox is a robust data annotation platform designed for scalability and efficiency in video labeling. It simplifies video annotation with its intuitive interface, active learning capabilities, and seamless integration into machine learning workflows, making it a preferred choice for teams of all sizes.

Main Features:

• Advance Video Annotation Types: Supports bounding boxes, polygons, keypoints, and semantic segmentation for precise labeling of objects, movements, and scenes in video datasets.
• Active Learning for Video Labeling: Utilizes model-assisted labeling and active learning to prioritize and automate complex annotations, significantly reducing manual effort.
• Customizable Workflows: Allows users to design annotation workflows tailored to specific video labeling needs, enabling efficient task management and progress tracking.

Pricing: Provides a free tier with limited features; enterprise plans are available for larger needs.

CVAT developed by Intel, is an open-source tool optimized for video and image annotation, offering a range of features suitable for beginners and expert annotators. It offers sophisticated features for performing various annotation activities like object detection, segmentation or tracking and therefore, is an ideal application for computer vision projects.

Main Features:

• Advance Video Annotation Types: Includes bounding boxes, polygons, key points, skeletons, and cuboids to handle diverse video annotation needs, from simple object detection to pose estimation.
• Automated Labeling: Leverages AI models such as YOLO, Mask R-CNN, and SiamMask for object detection and tracking, significantly speeding up video annotation workflows.
• Interactive Annotation Interface: Features keyboard shortcuts and interactive tools to enhance efficiency in frame-by-frame annotations, ideal for lengthy video datasets.
• Supports Multiple Formats: Compatible with a wide range of video formats like MP4, AVI, and MOV, along with export options in formats such as COCO and Pascal VOC for seamless integration with machine learning frameworks.

Pricing: Free and open-source.

6. Dataloop

Dataloop is a comprehensive video annotation, data management and automation platform. Designed for scalability, it supports diverse annotation tasks and integrates robust quality control tools to ensure accurate datasets for machine learning models. The cloud-based configuration also suits teams who are working on complex projects that require annotating videos at scale.

Main Features:

• Comprehensive Annotation Support: Enables bounding boxes, polygons, semantic segmentation, and other annotation types for detailed video labeling.
• AI-Assisted Video Labeling: Includes pre-built machine learning models and workflows to automate repetitive tasks and accelerate labeling.
• Advanced Quality Control Tools: Offers features like inter-annotator agreement analysis and validation metrics to maintain high annotation accuracy.

Pricing: Offers a free trial; detailed pricing is available upon contact.

7. RectLabel

RectLabel is a macOS-based annotation tool designed for efficient video and image labeling. With features tailored for object detection and tracking, it simplifies the process of creating high-quality datasets for machine learning projects. Its offline functionality makes it an excellent choice for macOS users working in secure or isolated environments.

• Object Tracking in Videos: Provides tools for annotating and tracking objects across multiple frames, allowing for consistent and accurate video labeling.
• Comprehensive Annotation Options: Supports bounding boxes, polygons, segmentation masks, and keypoints, enabling detailed annotations for objects and scenes in videos.
• Batch Processing: Speeds up workflows by enabling batch annotations and edits across multiple video frames.
• Format Compatibility: Offers export options in popular formats such as Pascal VOC, COCO, YOLO, and TensorFlow Object Detection API, ensuring seamless integration with ML frameworks.

Pricing: The standard RectLabel offers subscription plans, $2,99/month and $9.99/year.

8. VGG Image Annotator (VIA)

VIA is a lightweight, browser-based tool developed by the Visual Geometry Group at the University of Oxford. Designed for image and video annotation, it is accessible, free, and open-source, making it an excellent choice for researchers and developers seeking a no-frills solution for object detection and segmentation tasks.

Main Features:

• Diverse Annotation Capabilities: Supports bounding boxes, polygons, keypoints, and region descriptions for annotating objects and regions of interest in video datasets.
• Browser-Based Simplicity: Requires no installation or server setup, allowing users to annotate videos directly in their web browsers for maximum convenience.
• Batch Processing: Facilitates annotations across multiple video frames in bulk, improving workflow efficiency for large datasets.

Pricing: Free and open-source.

9. Supervisely

Supervisely is a comprehensive platform designed to streamline video annotation processes for machine learning and computer vision applications. It offers a suite of advanced tools and features that enhance the efficiency and accuracy of video labeling tasks.

Main Features:

• Versatile Annotation Tools: Supports bounding boxes, polygons, keypoints, and semantic segmentation for detailed video labeling.
• AI-Assisted Tracking: Automates object tracking across frames with intelligent model selection for different geometry types.
• Interactive Segmentation: Offers customizable, trainable neural networks for semantic segmentation, reducing manual effort.

Pricing: Offers a free trial; detailed pricing available on request.

10. Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth is a cloud-based application for data annotation that allows users to efficiently create quality labeled datasets in bulk. By combining fully automated and semi-automated labeling, it reduces the complexity of the video annotation task and provides seamless integration with the AWS environment for comprehensive ML project implementation.

Main Features:

• AI-Assisted Labeling for Videos: Automates video labeling with pre-trained machine learning models, reducing manual workload and improving efficiency.
• Human-in-the-Loop Annotation: Ensures precision by combining automated annotations with human reviews for quality control.
• Custom Annotation Workflows: Enables users to design workflows tailored to specific video labeling tasks, such as object tracking or segmentation.

Pricing: Pricing is based on usage, including per-hour costs for human labeling and AI-assisted labeling tasks.

Choosing the best video labeling tool begins with understanding your project requirements. Here are several things you need to consider:

Annotation Types

• Determine the types of annotations your project demands. For instance:
• For Autonomous Driving: Tools like Supervisely and Labellerr are excellent as they support bounding boxes, semantic segmentation, and instance tracking, essential for detecting vehicles, pedestrians, and road elements.
• For Healthcare Projects: Labellerr and SuperAnnotate provide keypoint annotations for tracking body movements or segmentation masks for medical imaging datasets.
• Check if the tool offers flexibility in switching between methods to accommodate evolving needs. For example, CVAT allows you to annotate with bounding boxes, polygons, and skeletons, making it versatile for a variety of use cases.

Automation Needs

• If your dataset is large, opt for a tool like Amazon SageMaker Ground Truth and Dataloop that supports pre-labeling and automation using AI models.
• Features like object tracking and automated segmentation reduce manual effort and accelerate the annotation process.
• Tools like Labelbox and DagsHub with active learning support can prioritize the most important video segments for labeling, saving time and resources while improving data quality.

Collaboration Requirements

• If your team is large, select a tool like Filestage or Dataloop, that supports real-time collaboration, role assignments, and progress tracking. This ensures consistent annotations and improves workflow efficiency.
• Cloud-based tools like Supervisely and DagsHub are ideal for distributed teams, enabling seamless collaboration regardless of location.
• Features like annotation reviews and version control in Labelbox ensure consistency across team annotations, while tools like Amazon SageMaker Ground Truth combine automated workflows with human-in-the-loop reviews for quality control.

Data Management and Integration

• Ensure the tool offers robust data management features, such as dataset curation, versioning, and storage integration. These are critical for organizing large video datasets efficiently.
• Tools like CVAT and RectLabel support multiple export formats, including COCO, YOLO, and Pascal VOC, ensuring compatibility with popular machine learning models.
• Choose tools that integrate with ML frameworks and platforms:
• Amazon SageMaker Ground Truth integrates with AWS for streamlined workflows.
• DagsHub connects with TensorFlow and PyTorch, providing version control for data and annotations.

Quality Assurance Features

• Tools with built-in QA mechanisms ensure consistent and accurate annotations:
• Labellerr and SuperAnnotate offer inter-annotator agreement analysis and consensus scoring to maintain quality.
• Filestage tracks annotation versions and facilitates feedback consolidation to ensure alignment.

The tools discussed in this article each offer unique strengths to meet different video labeling needs. For instance, DagsHub is ideal for collaborative machine learning projects, while Labellerr excels in automated labeling for large datasets. Tools like CVAT provide open-source flexibility, and Amazon SageMaker Ground Truth integrates seamlessly with AWS services for high-scale annotation.

By understanding your project requirements and leveraging the strengths of these tools, you can streamline your labeling workflows, improve data quality, and ultimately enhance the performance of your machine learning models.