Top 10 Best Video Annotation Software of 2026

CVAT (cvat.ai) is an open-source, web-based annotation platform specialized for computer vision tasks, offering robust tools for both image and video labeling. It stands out in video annotation with features like frame-by-frame labeling, object tracking across frames, automatic interpolation, and support for polygons, cuboids, and tags. Designed for scalability, it enables team collaboration, quality control workflows, and seamless integration with ML training pipelines via exports in COCO, YOLO, and other formats.

V7 Darwin is an AI-powered video annotation platform from V7 Labs that accelerates the creation of high-quality training data for computer vision models. It supports advanced annotations like object tracking, semantic and instance segmentation, keypoints, and classification across video frames, with auto-annotation models that adapt and improve through active learning from user feedback. The tool emphasizes scalability, collaboration, and integration into ML pipelines, making it suitable for production-grade datasets.

Labelbox is a robust data labeling platform designed for machine learning teams, offering specialized tools for video annotation including frame-by-frame labeling, object tracking, and segmentation. It supports complex ontologies for videos, enabling precise annotations for tasks like autonomous driving or surveillance AI. The platform integrates automation via pre-trained models and facilitates collaboration with quality control workflows.

Supervisely is a comprehensive computer vision platform specializing in annotation for images, videos, and 3D data, with robust video annotation capabilities including frame-by-frame labeling, automatic object tracking, and interpolation. It supports a wide range of annotation types like polygons, keypoints, brushes, and cuboids, enhanced by AI-assisted tools for efficiency. Designed for collaborative workflows, it integrates seamlessly with ML training pipelines, making it suitable for large-scale video labeling projects.

Encord is a data-centric AI platform specializing in high-quality annotation for computer vision tasks, with robust support for video data including object tracking, semantic/instance segmentation, keypoints, and classification across frames. It integrates active learning, model-assisted labeling, and quality control workflows to streamline the annotation process for ML teams. The tool emphasizes scalability, collaboration, and ontology management for complex video datasets.

Segments.ai is a powerful annotation platform specialized in labeling images and videos for computer vision training data. It offers advanced tools for video annotation, including object tracking, keyframe labeling, and automatic interpolation to propagate annotations across frames efficiently. The platform supports team collaboration, quality assurance workflows, and integrations with popular ML frameworks, making it ideal for scalable data labeling projects.

Label Studio is an open-source data labeling platform that supports multi-modal annotation, including comprehensive video labeling for machine learning projects. It offers tools for object tracking, semantic segmentation, keypoint annotation, and interpolation across video frames, enabling efficient labeling workflows. The platform is highly customizable through XML configurations and integrates with ML backends for active learning and model-assisted labeling.

SuperAnnotate is a powerful platform designed for creating high-quality training data for AI and machine learning models, with specialized tools for video annotation including bounding boxes, polygons, keypoints, and semantic segmentation. It supports frame-by-frame labeling, automatic object tracking, and interpolation to accelerate workflows while maintaining precision across long video sequences. The platform also features built-in quality control, team collaboration, and AI-assisted automation to ensure annotation accuracy and scalability for computer vision projects.

Scale Rapid, from Scale AI (scale.com), is a high-speed video annotation platform designed for labeling large-scale video datasets to train computer vision models. It supports advanced annotation types including bounding boxes, segmentation, keypoints, and temporal tracking across multiple frames. The tool leverages Scale's managed workforce and automation to deliver rapid, high-accuracy labels for ML workflows.

Dataloop is an end-to-end MLOps platform specializing in data management for AI, with robust video annotation capabilities for computer vision tasks. It enables precise labeling of videos through tools like bounding boxes, polygons, semantic segmentation, and object tracking across frames. The platform emphasizes scalability, automation via AI pre-labeling, and seamless integration into ML pipelines for collaborative team workflows.