Video annotation plays a fundamental role in the development and training of artificial intelligence models. It enables video sequences to be transformed into usable data by identifying, labeling and analyzing the key elements present in each image. This guide explores the solutions available on the market, including the best annotation tools, both free and paid, to meet the needs of AI professionals and ambitious projects.
Annotations (or metadata), often produced on a large scale, are essential for a variety of applications such as 🔗 object recognitionmotion detection and behavioral analysis. Faced with the rapid growth of these technologies, the choice of an appropriate annotation tool has become essential to guarantee the quality and efficiency of AI models. It is also important to know how to annotate a video to improve the clarity and engagement of visual content.
What is a video annotation tool?
A video annotation tool is software or an application that adds information, comments or annotations to a video. These tools are designed to improve the understanding and analysis of videos, particularly in the fields of education, training and research... and especially to train artificial intelligence models! The functionalities offered by these tools can vary, but generally include the ability to add subtitles, comments, labels or visual annotations at specific moments in the video.
For example, a teacher might use a video annotation tool to add explanatory subtitles to an educational video, while a researcher might mark specific segments of a video for further analysis. Annotations can take different forms, such as bounding boxes to identify objects, polygons to delineate areas of interest, or 🔗 key points to mark precise positions.
1 - CVAT: the essential open-source tool for video annotation
CVAT (Computer Vision Annotation Tool) is an open-source platform designed to meet the data annotation needs of artificial intelligence and computer vision projects. Renowned for its power and flexibility, CVAT is widely adopted by AI specialists looking to produce precise, detailed annotations on videos and images.
Efficient management of large volumes of data
CVAT excels at managing large annotation projects. Its advanced tools enable you to create complex annotations such as bounding boxes, polygons, key points and segmentation masks. The use of progress bars also facilitates the management of ambitious projects and large volumes of data.
CVAT also supports multi-frame object tracking (or 🔗 "Object Tracking"), making it an ideal choice for applications requiring temporal consistency, such as moving object recognition or behavioral analysis.
A customizable, intuitive interface
CVAT 's user interface is designed to be intuitive yet allow complete customization. Users can configure tools according to the specific requirements of their projects, from defining object classes to creating optimized annotation workflows . Its compatibility with a variety of file formats, including MP4 and AVI, makes it a versatile and adaptable solution.
A tool designed for collaboration
CVAT facilitates teamwork by allowing tasks to be shared between several annotators. Integrated review functions guarantee annotation quality throughout the process. Administrators can monitor progress and adjust workflows to ensure that projects progress efficiently.
Open-source and highly flexible
As an open-source product, CVAT offers total transparency and the ability to customize code to meet specific needs. This flexibility is particularly appreciated in academic and industrial environments, where projects often require tailor-made solutions.
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2 - LabelBox: a complete platform for collaborative video annotation
LabelBox is a dedicated data annotation platform, designed to create high-quality datasets for training artificial intelligence models. It stands out for its advanced collaborative features, enabling internal and external teams to work together in real time on video annotation projects.
A range of tools for precise video annotation
LabelBox 's video editor offers a variety of tools for precise video annotation. Users can apply bounding boxes, polylines, points or segmentation masks to identify and label specific objects or concepts.
Pixel-level video segmentation guarantees optimum precision in identifying elements of interest, which is essential for complex applications.
Automation withmodel-assisted labeling
To increase efficiency, LabelBox integrates automation tools such as 🔗 model-assisted labelingwhich uses predictive models to 🔗 pre-label data and thus generate text content.
This allows annotators to concentrate on correcting and perfecting annotations, thus considerably reducing processing time. This feature can increase overall efficiency by up to 80%.
Closer collaboration
Collaboration is at the heart of LabelBox. The platform offers customizable workflows for complex projects. Features such as problem reporting, sharing, comments and notes promote clear communication between teams.
These tools make coordination fluid, even for geographically dispersed teams.
Flexibility for diverse data types
Labelbox supports a variety of data types, including videos, images, subtitles, PDF documents, 🔗 geospatial data and audio. This flexibility makes it possible to manage a variety of projects within a single platform, avoiding dispersion between several tools.
Centralized management and access to experts
When it comes to management, LabelBox offers streamlined capabilities with optimized workflows to speed up dataset creation. Users can also 🔗 collaborate with expert annotators for specific projects, contributing to the creation and training of ever more powerful AI models.
3 - Dataloop: the all-in-one solution for annotation and data management
Dataloop is a comprehensive platform designed to simplify and accelerate annotation processes while centralizing project management. Developed specifically for teams working on computer vision and artificial intelligence projects, it combines advanced annotation tools, automation functionalities and optimized workflow management.
Extensive data format support
Dataloop is compatible with a wide range of formats, including videos, images, documents and 3D files. For video annotation, the platform offers powerful tools such as bounding boxes, semantic segmentation, polylines, and object tracking through frames. These features make it possible to process complex sequences while maintaining maximum consistency and precision.
Automation for time-saving precision
One of Dataloop's strengths lies in its automation capabilities. By integrating machine learning models directly into the workflow, the platform enables efficient pre-labeling of data. This automation significantly reduces the time required for manual annotation, while allowing users to refine and correct results to ensure optimum quality.
Project management and real-time tracking
Dataloop includes robust project management tools. Administrators can structure workflows, assign tasks, and track progress in real time via detailed dashboards. These tools can also assess annotation quality and monitor annotator performance, facilitating strategic adjustments to improve project efficiency.
Cloud-enhanced collaboration
Thanks to its cloud infrastructure, Dataloop facilitates collaboration between teams, even when they are geographically dispersed. Data and annotations are accessible in real time, enabling teams to work simultaneously on the same project. Adjustments and revisions are simplified, reducing delays and improving coordination.
4 - V7: the AI-assisted video annotation platform
V7 (or V7 Labs, or otherwise known by its product name, "Darwin") is a cutting-edge platform dedicated to data annotation for training artificial intelligence models. It offers powerful tools for processing images and videos, while integrating advanced automation and collaborative project management features.
Precise, efficient video annotation
V7 supports a wide range of annotation tasks, including object detection, 🔗 semantic segmentation and 🔗 pose estimation. These tools enable video sequences to be annotated quickly and accurately, guaranteeing consistent annotations across all frames. Thanks to its object tracking system, V7 considerably simplifies complex projects requiring temporal annotation.
Automation thanks to artificial intelligence
One of V7 's strengths is its integration of machine learning models into the annotation process. These models enable data to be pre-labeled, reducing the time spent on manual annotation. Users can adjust and refine the results generated by the AI to guarantee high-quality annotations, while optimizing production times.
An intuitive, customizable interface
V7 features a modern, intuitive user interface, making it easy to navigate and work collaboratively. Annotators can customize the tools to their specific needs, from creating object classes to adjusting task parameters. This flexibility makes V7 suitable for a variety of sectors, including healthcare, robotics and automotive.
Real-time collaboration and centralized management
With its collaborative features, V7 enables multiple annotators to work simultaneously on the same project. The platform includes options for tracking progress, assigning tasks and ensuring real-time quality control. These tools are ideal for dispersed teams or projects requiring rigorous workflow management.
5 - SuperAnnotate: a collaborative solution for advanced video annotation
SuperAnnotate is a complete platform dedicated to the annotation of visual data, offering advanced tools for image and video annotation. Designed to facilitate collaboration and improve the efficiency of artificial intelligence projects, it is aimed at teams seeking to produce high-quality datasets for model training.
Advanced video annotation tools
SuperAnnotate supports a variety of video annotation tasks, including :
- Object tracking and localization: Use bounding boxes to detect and track the movement and position of objects across consecutive frames.
- Action detection: Recognition and detection of specific actions or activities, even those that can't be defined by a simple bounding box, such as rain or traffic.
- Pose estimation: Determining the position and orientation of joints and body parts.
- Segmentation and tracking of instances: Identify and track individual instances within a video sequence.
- Lane detection: Identification and tracking of lanes on a road in videos.
These features enable precise, detailed annotation, essential for the development of high-performance AI models.
Automating and accelerating the annotation process
To optimize time and resources, SuperAnnotate integrates automation tools such as :
- Autotrack: Partial automation of the video annotation process by predicting the movement of one or more moving objects.
- Interpolation: prediction of the movement of an instance between two frames, speeding up the video annotation process.
These tools significantly reduce the time needed to annotate long video sequences, while maintaining high accuracy.
Real-time collaboration and quality management
SuperAnnotate facilitates collaboration between team members thanks to features such as :
- Feedback system: Allows stakeholders to participate in the annotation quality review process, making collaboration and task distribution smoother than ever. In addition, collaboration with partners is essential for improving workflow and productivity.
- Item and project status: Overview of the status of all items and projects to track their progress.
- Detailed instructions: Provision of clear guidelines to the team to ensure successful project execution.
These tools guarantee effective communication and rigorous management of annotation quality.
Integration and flexibility
The platform is compatible with a variety of video data formats, making it adaptable to a multitude of use cases, such as autonomous driving, robotics, surveillance and many others. In addition, SuperAnnotate offers integrations with machine learning models, enabling increased automation and continuous improvement of annotation processes.
6 - Amazon SageMaker Ground Truth: video annotation powered by AWS
Amazon SageMaker Ground Truth is an AWS-managed service that facilitates the creation of annotated datasets for training artificial intelligence models. It offers specific features for video annotation, optimizing the visual data preparation process.
Video annotation task types
Ground Truth offers three types of integrated tasks for video annotation:
- Video clip classification: Allows annotators to classify entire videos into predefined categories, such as sports, comedy or education.
- Object detection in video frames: Annotators identify and locate specific objects in frames extracted from videos using bounding boxes, polylines or polygons.
- Object tracking in video frames: This task involves tracking the movement of objects across multiple frames, ensuring the temporal consistency of annotations.
Automation and efficiency
Ground Truth incorporates automation features that reduce the time and costs associated with manual annotation. Using machine learning models, the service can pre-label data, allowing human annotators to concentrate on validating and adjusting annotations, improving overall process efficiency.
User interface and collaboration
The platform offers an intuitive user interface for annotators, with tools tailored to each type of task. However, unlike Google Docs, which presents limitations when entering suggestions, our tool enables seamless integration with advanced annotation features. Worker instructions can be customized for each project, ensuring a clear understanding of requirements. What's more, Ground Truth enables collaboration between multiple annotators and centralized project management, facilitating progress tracking and quality control.
Integration with the AWS ecosystem
As an AWS service, Ground Truth integrates seamlessly with other AWS services, such as Amazon S3 for data storage and Amazon SageMaker for model deployment. This integration simplifies the workflow, from data preparation to training and deployment of AI models.
7 - Encord: a specialized video annotation and tracking tool
Encord is a platform dedicated to video annotation and tracking, designed to meet the needs of artificial intelligence projects requiring accurate, reliable data sets. Its advanced tools simplify and accelerate the annotation process, while guaranteeing high-quality results.
AI-assisted annotation
Encord integrates artificial intelligence-assisted annotation software, enabling videos of various formats (MP4, AVI, MOV, WebM) and lengths to be tagged. This feature offers invaluable flexibility for teams working on complex projects, and facilitates integration of the platform into a variety of workflows. AI-driven pre-labeling significantly reduces manual work, while improving accuracy and efficiency.
Advanced tools for monitoring and continuity
One of Encord's strong points is its ability to manage object tracking and interpolation between frames. This feature guarantees the consistency of annotations throughout the video sequence, a key requirement for applications such as object detection or semantic segmentation. Thanks to these tools, users can annotate complex videos quickly and accurately.
Versatile subtitle support
Encord offers a variety of annotation types, including classification, object detection, segmentation and even pose estimation. This diversity makes it possible to create rich datasets, suitable for a wide range of computer vision applications, such as image recognition or behavioral analysis.
Efficient workflow management
The platform features customizable workflows that facilitate the management of annotation projects. Users can assign tasks, monitor annotators' performance, and configure review and quality control pipelines.
8 - Scale AI: high-quality video annotations for your AI models
Scale AI specializes in high-quality annotated data for training artificial intelligence models. It offers a range of services tailored to the needs of companies developing AI-based applications, particularly in the fields of computer vision, autonomous driving and augmented reality.
Video annotation services
Scale AI offers advanced video annotation services, enabling the precise labeling of moving objects in video sequences. Using a combination of artificial intelligence and human intervention, the company ensures fast and accurate annotation, essential for the effective training of AI models.
Optimized annotation pipeline
To handle the annotation of long videos, Scale AI uses a process called "video stitching". This method involves dividing long video sequences into several smaller sub-tasks, which can be processed in parallel. The results are then stitched together to create a coherent annotation of the whole video, making annotation of videos longer than 30 frames more operational.
Quality and precision
Scale AI's platform combines machine learning algorithms with enhanced statistical controls to guarantee annotation quality. This hybrid approach makes it possible to process large amounts of data while maintaining a high level of accuracy, which is important for the development of reliable AI models.
Versatile applications
Scale AI's services are used by leading companies in various sectors, such as autonomous vehicles, mapping, augmented/virtual reality and robotics. By providing high-quality annotated data, Scale AI helps these companies accelerate the development of their AI-based applications.
9 - VGG Image Annotator (VIA): a lightweight open-source video annotation tool
The VGG Image Annotator (VIA) is an open-source annotation solution developed by the Visual Geometry Group at Oxford University. The software is lightweight, accessible and easy to use, making it an ideal tool for researchers and developers working on projects requiring fast, efficient annotation.
No installation required for ease of use
One of VIA's key features is that it requires no complex installation or configuration. It runs directly in a web browser, eliminating compatibility issues with different operating systems.
What's more, annotations and data are saved locally, guaranteeing confidentiality and total control over files. This cloud-free approach is particularly suited to environments where data security is paramount.
A wide range of functions for annotating images and videos
When it comes to features, VIA lets you annotate both images and videos. Users can create bounding boxes, polygons, key points or segments to precisely mark objects or areas of interest.
The tool also offers options for adding metadata to annotations, which is useful for enriching datasets with additional information.
Flexibility and customization
VIA is also renowned for its flexibility. Thanks to its minimalist interface, it can be customized to suit specific user requirements. For example, developers can modify the source code to integrate additional functionalities or adjust the tool to particular data formats.
A powerful but limited tool for advanced projects
Although its simplicity is a major advantage, VIA may not be suitable for very complex projects requiring advanced features, such as object tracking across multiple frames or integration with real-time collaborative workflows. However, for basic to intermediate annotation needs, it remains a powerful, free and easy-to-use tool.
10 - Supervise.ly: advanced features for precise video annotation
🔗 Supervise.ly is an innovative platform specially designed for projects requiring precise video annotations. With a full range of advanced features, it is a must-have tool for developers and teams working on complex artificial intelligence models.
An intelligent self-tracking system
Supervise.ly stands out for its auto-tracking system, powered by predictive models. This technology automatically identifies and tracks objects in a video, whether using bounding boxes, skeletons or segmentation masks.
Auto-tracking adjusts annotations frame by frame and detects when an object leaves the scene, reducing the time needed to annotate long sequences. According to the editor, this feature can increase productivity by up to 500%.
An intuitive interface for efficient management
Supervise.ly 's interface is designed to simplify workflows. The definition panel enables a quick search and instant selection of the object classes to be annotated, avoiding the need to constantly navigate between tools. This ergonomic design considerably enhances the user experience, making complex projects smoother to manage.
A flexible tagging system
Supervise.ly offers a simplified tagging method, enabling users to add global or specific tags to each image or video. This system is particularly useful for enriching annotations with detailed metadata, facilitating analysis and database use.
A combination of automation and human control
One of Supervise.ly's great strengths lies in its ability to balance automation and human control. Repetitive tasks can be handled by artificial intelligence, while users retain the option of manually refining annotations to achieve optimum accuracy.
How does video annotation outsourcing work?
Video annotation outsourcing involves entrusting the task of annotating videos to external service providers. This approach is particularly useful for companies or organizations that need to process a large volume of videos but lack the internal resources or skills to do so efficiently.
Video annotation service providers use specialized video annotation tools to add annotations and comments to videos. These annotations can include subtitles, labels, bounding boxes, polygons, and other forms of visual annotation. Once completed, the annotated videos are returned to the client company or organization.
Outsourcing offers a number of advantages, including the ability to manage large volumes of data in a short space of time, and to benefit from the expertise of professionals specialized in video annotation. However, it can also pose challenges, such as the need to guarantee the quality and consistency of annotations, as well as managing data confidentiality and security.
The challenges of using video annotation tools
Using video annotation tools can present several challenges, including:
- Quality of annotations: Ensuring that annotations are accurate and consistent is crucial to avoid errors or misunderstandings. Poor quality annotation can lead to incorrect results when training artificial intelligence models, compromising project efficiency.
- Data management: Video annotation tools can generate a large volume of data, requiring an effective data management system to store, organize and analyze this information. Inadequate data management can lead to data loss or difficulties in retrieving specific annotations.
- Security: Annotated videos can contain sensitive information, so it's essential to take security measures to protect this information. This includes using robust security protocols for data storage and transfer, and implementing strict access controls to limit access to sensitive data.
To overcome these challenges, we recommend adopting best practices such as training annotators, using advanced data management systems and implementing rigorous security measures.
How do you choose the best video annotation tool for your needs?
To choose the best video annotation tool for your needs, it's important to consider the following factors:
- Features: What kind of annotations and comments do you want to add to your videos? Make sure the tool offers the necessary functionality to meet your specific needs, whether it's subtitles, bounding boxes, polygons or other forms of annotation.
- Annotation quality: What quality standards do you want to achieve for your annotations? Look for tools that offer quality control features such as peer review or automatic verification algorithms.
- Data management: How will you manage and analyze the data generated by video annotation tools? Opt for tools that offer robust data management solutions, including secure storage, search and annotation retrieval options.
- Security: What are the security risks associated with using video annotation tools, and how will you mitigate them? Choose tools that emphasize data security, with strict encryption protocols and access controls.
💡 By considering these factors, you'll be able to choose the video annotation tool that best suits your needs and objectives, which should subsequently guarantee the success of your video annotation projects.
Conclusion
Video annotation is a fundamental step in the creation of high-performance artificial intelligence models, and choosing the right tool can greatly influence the quality and efficiency of your projects. Whether you're looking for an open-source solution like CVAT or a comprehensive, collaborative platform like Labelbox, each option presented in this top 10 addresses specific needs, whether related to accuracy, automation or workflow management.
These tools offer a wide range of functionalities, from simple annotations to advanced solutions incorporating machine learning models to speed up the process. They are aimed at a wide audience, from researchers and professionals to companies seeking to optimize their data for complex AI applications.
The choice of the ideal solution will depend on your specific requirements: data volume, project type, level of collaboration or available budget. By exploring these options, you'll be able to find the tool that integrates perfectly with your workflows and enables you to maximize your results.
With technologies constantly evolving, these platforms continue to have a new regular update, making video annotation more accessible, fast and accurate. By adopting one of these tools, you give yourself the means to take your artificial intelligence projects to the next level.
