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Video annotation is used in creating training data sets for high visualization training in deep learning and machine learning models. Video annotation services involve adding metadata to videos which can be used to train Computer Vision models to detect and identify moving objects. It involves a very intensive process of processing, analyzing and understanding every frame in a video. We use highly specialized tools and a dedicated team of experts to ensure your video annotation needs are fulfilled. Our state of the art video annotation service lets you train your algorithm to perform a variety of tasks that are required for your AI technology and computer systems.

Video Annotation

Purpose of Video Annotation Services

  • Detecting Objects: Identifying objects of interest through each frame and making them recognizable to machines.
  • Localize Objects: Locating main objects in an image where multiples objects might be visible at the same time
  • Track Objects: Detecting and recognizing a wide variety of objects that are in motion.
  • Track Activities: Training computer vision based AI to track human activities and estimate poses.

How Video Annotation Works

Labelling or annotation makes the object of interest recognizable while feeding it into an algorithm. In video annotation, this means making moving objects recognizable and learning their movements through computer vision. The size and complexity of data contained within a video makes annotation a formidable task. There are two methods to approach video annotation -

  • Single Image Method: In this method the video is broken down into a series of image frames which are then annotated one frame at a time. This method is time consuming and inefficient and can lead to lesser quality due to chances of mislabelling when moving through the hundreds or sometimes, thousands of image frames.
  • Continuous Frame Method: In this method, we will make use of automation tools to accurately streamline the video annotation process. The computer will analyze pixels of corresponding frames and predict its movement throughout the whole video. This will preserve the continuity and flow of information captured from the video.
  • Incorporating automation components in the process can increase the accuracy and reliability of the data at a much higher efficiency.

Use of Video annotation

Video annotations are used in training Computer vision based AI models that have virtually unlimited applications. Some common usage for it include-

  • Self Driving Vehicles: Video annotation helps autonomous vehicles to recognize and track objects like vehicles, pedestrians, traffic lanes, lights, etc.
  • Medical AI: With deep learning technology, AI makes use of video annotations to identify possible diseases and prepare reports. It can assist medical experts in repetitive activities that are homogenous and prone to errors by increasing quality and efficiency.
  • In Sports: Video annotation is used in training AI or machine learning models to track the actions of athletes during sport events which can then be used to extract valuable information such as analyzing performance and predicting injuries.

Why use video annotation?

Computer vision models that make use of annotated video provide greater results than ones that are trained on image alone. It provides your algorithm with more information that it can learn to identify and make connections with. This means more efficient and reliable production on your end.

We have assembled a team of professionals that provide our customers with consistently reliable and comprehensive video annotation tools and services. We provide top quality training data for exceptional AI and machine learning projects through a combination of exceptional human intelligence and state of the art technology. Give a boost to your project efficiency and gain access to our testing ground for existing models with our labeling tool.