Video Annotation Services for Computer Vision

To help computer vision models accurately determine moving elements' positions and orientations, we place rectangular boxes around the objects of interest (such as pedestrians, lanes, potholes, signals, people, bags, and animals) in visual data. Through the 2D bounding box video annotation technique, we enable precise object categorization and tracking by AI systems used in autonomous vehicles, security surveillance, and traffic monitoring.

Through temporal segmentation, we divide minute-long videos into individual frames and categorize them based on semantic context. Our annotators assign a predefined label to every pixel, ensuring detailed scene comprehension by machine learning models used for applications like medical imaging, robotics, and precision farming.

For the AI model's precise depth estimation and spatial reasoning, we label objects in 3D images with cuboid bounding boxes. The data labeled through this video annotation technique enables AI-powered computer vision systems to interpret the orientation, size, and position of objects-such as vehicles, furniture, and industrial equipment-in a three-dimensional space.

We annotate facial landmarks (mouth, nose, eyes, etc.,), joint positions, and key points across targeted objects to help AI systems recognize human activity and interpret body movements. Keypoint annotation is particularly useful in facial recognition, security surveillance, virtual try-on, and sports & motion analysis.

For objects with complex, irregular boudaries-such as traffic signs, medical scans (tumors, organs), human figures, buildings, and agricultural products-we employ polygon annotation. However, to label linear structures (such as roads and lanes), we utilize polyline annotation, facilitating precise lane identification and navigation by ADAS (advanced driver- assistance systems) applications in self-driving cars.

We localize events of interest in multiple frames and assign relevant labels to video clips belonging to a particular class. This labeled video data enables AI systems to understand what type of activity is happening during different segments of the video and identify key actions, particularly useful in healthcare monitoring and sports analytics.

The data points collected from depth cameras and LiDAR sensors get annotated by our experts for accurate three-dimensional environment mapping. By labeling 3D point clouds with attributes such as object class, size, shape, orientation, and position, we enhance the AI system's ability to recognize objects and understand complex scenes in real-world settings.