Video Annotation Types for AI & ML Models
We understand that diverse video annotation projects require specific labeling techniques depending on their intended objective. By thoroughly understanding your AI model's goals and project requirements, we select the optimal video labeling method and provide reliable training datasets.
2D & 3D Bounding Box
We draw 2D bounding boxes around the objects of interest in a video for efficient object localization and tracking. This technique is widely applied to create training datasets for applications used in facial recognition, retail inventory management, and wildlife monitoring. For more complex cases, we utilize the 3D bounding box (or cuboid annotation) technique. By drawing cuboids around the objects in three-dimensional space, we enable applications in autonomous vehicles, robotics, and augmented reality to accurately depict their width, length, and depth.
Semantic Segmentation
Our experts label each pixel in a video frame to categorize parts of the image into semantic classes for meaningful interpretation. By annotating multiple objects in a frame at the pixel level, we enable machine learning models to make more accurate predictions and better understand visual data for industrial inspection, environmental monitoring, and other use cases.
Polygon Annotation
To label objects that do not fit neatly into rectangular boxes, such as vehicles, road signs, or human cell structures, we utilize polygon annotation. By creating precise outlines around irregular objects using multi-point polygonal shapes, we create training datasets for AI models used in geospatial analysis, medical imaging, and robotics.
Keypoint & Landmark Annotation
We annotate facial features, such as mouth, nose, and eyes, as they appear in each video frame by placing and connecting key points. Landmark annotation is particularly useful in facial recognition, pose estimation, and security surveillance, helping AI systems precisely interpret facial expressions and body movements.
Polyline Annotation
To label linear structures in videos, such as roads, lanes, and boundaries, our experts employ polyline annotation. This technique is particularly useful in creating training datasets for autonomous driving systems, enabling accurate lane identification and navigation along paths and roadways.
3D Point Cloud Annotation
We specialize in labeling data points collected from 3D sensors like LiDAR. By annotating individual points or clusters in a three-dimensional environment, we enhance the spatial analysis capabilities of AI models used for urban planning, robotics, environmental monitoring, autonomous vehicles, and more.
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