Video Annotation.

Video annotation, the term might seem familiar to those working with technologies like machine learning and artificial intelligence. However, for a naïve, it is no less than technical jargon. In simple words, it is the process of labeling video clips to make a dataset that further trains machine learning, artificial intelligence, or deep learning models.

These trained models are then used for computer vision to classify videos automatically. The prime aim of the video annotation is to identify the moving objects in a video and make it recognizable. Besides this, there are many other reasons for which video annotation is used deep learning (a subset of artificial intelligence). Here are the four of them:

1.Tracking Objects for Self-Drive Vehicles

The arrival of technologies like artificial intelligence, machine learning, natural language processing, and more has made it possible to drive a vehicle without any human. However, there are tons of techniques and processes that work in the background to ensure that everything works fine. 

Video annotation trains the visual perception AI model that is designed specifically for driverless vehicles. With the training, the model detects and tracks different types of objects in the surroundings of the vehicle. The cars can automatically detect objects like street lights, zebra crossing, pedestrian, signboards, other vehicles running on the road, cyclists, and more. 

2.Detecting Objects Frame-By-Frame

Video annotation can capture an object frame-by-frame, therefore, making it easily identifiable by a machine. The process creates frames around the moving objects (running in a video) and annotates them with the help of specialized tools to ensure precision in detection. This annotation helps in training the machine learning, deep learning, and artificial intelligence models. 

Object Localization 

Object localization is one of the prime purposes of video annotation. The process is performed to locate the main or focused object in a video that contains several other objects. To do so, video annotation creates boundaries around the focused object so that it can easily be predicted by deep learning, AI, or ML models. 

Detecting and Tracking Activities and Poses of Human Beings

It is yet another significant purpose of video annotation in deep learning and computer vision. The process trains the model to detect and track activities as well as the posture of human beings. This feature of video annotation finds it use mainly in the sports field to identify and track the players’ actions during a match. 

Now, when you know about the uses of video annotation in deep learning, let’s check out what automatic video labeling is:

With a huge amount of data available, it is not easy to label objects of a video manually. Here is when automatic video labeling comes into the picture. The process of AVL includes machine learning and deep learning models that are pre-trained with the datasets (containing information about the objects) to be used for computer vision. These pre-trained models categorize the sequences of video clips (fed during the training of the models) in a particular group of classes. 

How Video Datasets are Interpolated in Deep Learning?

It can be done through these popular ways:

  • Bidirectional predictive network 
  • PhaseNet
  • Adaptive separable convolution
  • Deep voxel flow
  • Adaptive convolution

To sum up, we can say that the video annotation process trains the AI, ML, and deep learning models that are used in self-driving cars and drones to find out, label, and locate different types of objects. Since every business is upgrading itself with the latest technology, the popularity and usage of video, data, and image annotation is gaining popularity. There are many ML development companies that offer video annotation for deep learning services so that a customer can make the most of these technologies. 

The Bottom Line

Video annotation is used to identify and label the objects in motion in a video. The technique is not a new term for artificial intelligence and machine learning engineers. Apart from just recognizing objects, the process serves many other purposes. In this article, we have listed them all. Read it carefully to know the uses of video annotation in deep learning, which is further a part of artificial intelligence. 

However, if you are looking out for data annotation, image annotation, or video annotation services for AI, ML, Computer Vision, or Deep Learning, then reach out to a custom mobile app development company that holds expertise in these technologies. 

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