Comprehensive Video Annotation Services for AI and ML Models
Our highly skilled video annotation team collaborates with organizations to assist them in labelling and tagging video frames that will support and enhance the training of their AI and machine learning models.
Professional Video Annotation Services for AI Models
Video annotation is the process of labelling and tagging frames in a video to train artificial intelligence (AI) and machine learning (ML) models to recognize objects, actions, and motion. This is a crucial service to train computer vision models to better comprehend dynamic visual data – be it to autonomous vehicles, video surveillance, sports analytics, and more. Accurate video annotations can detect and analyse video footage to help AI systems demonstrate better decision-making.
Video annotation can improve model accuracy, provide real-time object tracking, and provide better action recognition. By creating quality annotated data for an AI model, a business can physically improve the capabilities of their AI in multiple areas, allowing for improved efficiency, reduced error, or better automation.
At Statswork, we provide tailored video annotation services using advanced educational tools to properly label and explain the video and its contents. Our highly trained staff guarantees fast, orderly, and scalable annotations that provide quality data to help allow AI models to train to their fullest potential.
At Statswork, we provide various video annotation services for many applications of AI. We have the experience to make sure that the annotation is both accurate and effective, no matter how complicated the project.
Object Tracking, Frame-by-Frame
We deliver frame-by-frame object tracking annotation, which means objects are identified across multiple video frames. This is essential for use cases such as surveillance, autonomous driving, and sports analysis, where moving objects require real-time tracking.
3D Bounding Boxes/Cuboids
We provide annotation of 3D bounding boxes to label objects that are not planar to the camera screen or are more complex than a basic bounding box. This annotation will help you with applications such as cinematography, robotics, and autonomous driving, where acknowledgment of the spatial placement of objects in 3D world space is knowledge, you may want to operate safely and efficiently
Semantic Video Segmentation
We offer semantic segmentation of a video, which simply means we segment the video into different regions and label those regions with a certain class, e.g., cars, pedestrians, road, sky, etc. This is important for scenarios such as autonomous vehicles or drones where the AI must understand components in the environment and acts on them.
2D & 3D Point Cloud Annotation
We also provide 2D & 3D point cloud annotation for videos or images taken from a LiDAR or depth camera. This includes annotating objects and labelling them in 3D space which helps autonomous vehicles improve AI model accuracy for navigation, robotics, and geospatial analysis.
Industries
Data collection allows sectors to train computer vision models, improve automation, improve diagnostics, ensure safety, and spur innovation via AI applications.
The process of labelling the individual frames of video data in order to create artificial intelligence (AI) models capable of understanding video data is called video annotation. The video annotation process consists of the following tasks:
Video Data Collection: Video Data is gathered from video sources (some examples are cameras, drones, or security/monitoring systems).
Annotating Video Frames: Each frame of video is annotated using tags, bounding boxes, or key points.
Quality Assurance: The labelled video frames are evaluated for accuracy and consistency.
Export and Create Dataset: The labelled video frames are exported in an organized format such as XML or JSON so it is capable to be used for AI training.
Inputs and Outputs in Video Annotation
Inputs: Unlabelled video data from cameras, drones, or monitoring systems
Outputs: Tagged video data (video frames) with tags, bounding boxes, or segmentation masks for AI training.
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