We provide comprehensive video labeling and classification services, where we tag objects, actions, or scenes in video frames with specific labels. This process helps AI systems identify and categorize key elements, enhancing video analysis for use in content moderation, activity recognition, and automatic tagging. Our services help AI companies develop models that can effectively process and analyze video content.

Bounding box annotation is a critical service for training AI models to recognize and locate objects within videos. By drawing boxes around objects of interest, such as people, vehicles, or animals, we provide precise visual data that helps your models understand object detection and tracking. This service is essential for applications like autonomous vehicles, security surveillance, and video analysis, enabling AI to accurately detect, track, and interpret objects in dynamic video environments.

Our polygon annotation services are designed to offer more precise labeling of complex shapes in videos. By outlining objects with irregular shapes, such as human figures or vehicles, we help AI systems detect and track objects in a more accurate way. This service is ideal for applications like advanced object detection and video surveillance, where detailed object recognition is crucial.

With semantic segmentation, we label every pixel in video frames, categorizing them by object type. This in-depth approach is essential for applications that require fine-grained scene understanding, such as autonomous driving or medical imaging. Our video annotation services help AI companies achieve accurate object detection and environment analysis, ensuring high-performance models.

Our instance segmentation service separates individual object instances in a video, providing both pixel-wise segmentation and distinct object identification. This allows your AI systems to distinguish between similar objects within the same category. Ideal for complex applications like facial recognition and multi-object tracking, this service enhances accuracy and object differentiation in real-world scenarios.

We offer panoptic annotation, which combines semantic and instance segmentation to label both stuff (e.g., roads, sky) and things (e.g., cars, people) in videos. This holistic approach is vital for applications requiring comprehensive scene understanding, such as autonomous vehicles or robotics, where distinguishing between multiple object types and instances is crucial for decision-making.

Our 3D cuboid annotation services allow us to create 3D bounding boxes around objects in video footage, providing depth information alongside traditional 2D detection. This service is ideal for applications in autonomous driving, robotics, and augmented reality, where spatial understanding and precise object localization in 3D space are essential for AI model performance.

Our polyline annotation service labels and tracks linear objects or paths in video frames. This service is commonly used for applications like road tracking in autonomous vehicles or mapping paths in satellite imagery. By providing precise path information, we help your AI systems accurately track and analyze continuous objects across video frames.

We provide skeletal annotation for tagging key points on the human body to create a skeletal structure. This service is essential for applications in human pose estimation, activity recognition, and biomechanics analysis. By tracking the movement of key points in videos, we help your AI models understand complex human actions and improve applications in healthcare, fitness, and entertainment.

Keypoint annotation involves labeling specific points on objects or human bodies within video frames, such as joints in human pose estimation or facial features for facial recognition. This service is critical for AI applications in gesture recognition, action detection, and biomechanics, where accurate localization of key features enables effective motion analysis.