What Is The Use And Purpose Of Video Annotation In Deep Learning?
Just like image annotation, video annotation also helps machines to recognize the objects through computer vision.
Basically, the main motive of video annotation is detecting the moving objects in the videos and makes it recognizable with frame-to-frame outlining of objects to train the AI models developed with deep learning.
Use of Video Annotation
Apart from, detecting and recognizing the objects, which are also possible through image annotation, there are various reasons video annotation is used in creating the training data set for visual perception based AI models observe varied objects.
Actually, these models get trained through an algorithm to perceive the various types of objects through video annotation service. So, right here, apart from object detection, we will explain what is the use and purpose of video annotation in deep learning.
Frame-by-Frame Objects Detection
The first and most use and purpose of video annotation is capturing the object of interest frame-by-frame and making it recognizable to machines.
The moving objects run on the screen annotated using the special tool for precise detection through machine learning algorithms used to train the visual perception based AI models.
Object Localization for Computer Vision
Another use of video annotation is localizing the objects in the video. Actually, there are multiple objects visible in a video and localization helps to locate the main object in an image, means the object mostly visible and focused in the frame.
Actually, the main task of object localization is to predict the object in an image with its boundaries.
Object Tracking for Autonomous Vehicle
Another important use of video annotation is help visual perception AI model build for autonomous vehicle is after detecting and recognizing the objects track the varied category of objects.
Video annotation helps self-driving cars to detect objects like pedestrians, street lights, sign boards, traffic lanes, signals, cyclists and vehicles moving on the road while self-driving cars is running on the street.
Tracking the Human Activity and Poses
Another significant purpose of video annotation is again to train the computer vision based AI or machine learning model track the human activities and estimate the poses.
This is mainly done in sports fields to track the actions athletes perform during the competitions and sports events helping machines to estimates the human poses.
These are various use of video annotation, and all these are done for the computer vision to train the visual perception based model through machine learning algorithms.
In self-driving cars and autonomous flying drones, video annotation is mainly used to train the model for precise detection, recognition and localization of varied objects.
There are many video annotation companies providing the data labeling service for AI and machine learning.
If you need a video annotation for deep learning, you can get in touch with Anolytics, that offers a world-class video annotation service to annotate the object of interest with frame-by-frame annotation at best level of accuracy.
This story was also published at www.vsinghbisen.com
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