Video Annotation Services for Computer Vision
Video annotation plays a major part in training computer vision models. However, breaking a video into minute frames and annotating each with the right metadata is tedious and time-consuming. It's more challenging than image annotation because of the inherent semantic complexities, volume of data in videos, hundreds of possible classifiers, and data set quality compliances, among other things.
Instead, businesses want a cheaper and faster way to create large volumes of meaningful and accurate annotations on video while preserving efficiency. That’s why it makes better business sense to outsource video annotation services for machine learning/artificial intelligence projects.
SunTec India- The Perfect Outsourcing Partner for Video Annotation Services
Train your computer vision models with accurate video annotation services from SunTec India.
Our professional annotators and data specialists understand the biggest challenges in video annotation and combat them with the right tools, techniques, and expertise. We employ an efficient and convenient annotation framework that can be adapted to your deep learning model’s relevant use cases.
In addition to frame-by-frame analysis of videos, object detection, recognition, and metadata annotation, our annotation services for computer vision models also facilitate rapid scaling. We are certified with ISO 9001:2015 for Data Quality and ISO 27001:2013 for Information Security, which helps ensure smooth integration and high-quality services without compromising on confidentiality.
A Comprehensive Range Of Video Annotation Solutions for Computer Vision/AI Training Data
Using various voice, text, and image annotation techniques for object localization, we locate the most visible individual objects and their boundaries from every frame in a video.
We employ an array of video annotation tools for computer vision models to detect all the objects of interest frame per frame and make them recognizable to machines through appropriate classification.
Labor Time Reduction
Our video annotation services follow the best industry practices, adapt to your model’s learning environment, and utilize the most effective annotation techniques(bounding boxes, landmark, semantic segmentation, polygons) to reduce the time and effort while preserving the quality of service.
With an operational workflow that ensures precise annotation and tracking of subsequent frames, we detect human figures and strides and use that classification to create a training data set that can coach a visual perception model on tracking human activities and estimating poses.
Video Annotation Services: Object Tracking for Autonomous Vehicles
Our professional annotators are adept at creating an all-inclusive video data set with perfectly labeled and tagged object segments, context descriptions, scene descriptions, optical flow annotations, and instance-wise segmentation. Such versatile data sets enable our clients to train their visual perceptions models and ML/AI algorithms with the most relevant cues.
The most prominent use cases of our video annotation services include autonomous vehicles and drones, as well as in the domains of robotics, AR, security, and healthcare. For instance, in self-driving cars, our accurate and reliable data sets can facilitate precise detection and recognition of obstacles on the road, assist navigation, and enhance its capability to predict movement on the path and chart an efficient course accordingly.
Activity Tracking and Pose Estimation: Video Annotation Services
Understanding human movement in videos is a massive step for deep learning computer vision models. By localizing human joints and tracking varied body poses in videos, followed by appropriate tagging, the resultant data set can be used in animation, analyzing player movements, action recognition, gaming, etc.
At SunTec India, we implement the latest and most effective approaches for video annotation to achieve better estimates. Our annotation experts locate the visible keypoints through a combination of manual and automatic video annotation techniques and work on newer approaches to estimate the occluded keypoints. We also analyze the estimates frequently and use iterative measures to correct the weaknesses in the data set model.