Video Annotation: What Is It and How It Benefits Your AI ML Model
- 2022-07-07, 07:41 am
- Video Annotation
- Video Annotation
The development of Artificial Intelligence (AI) and Machine Learning (ML) is largely driven by training data sets customized to a model’s training requirements. These help companies’ ML algorithms detect objects and memorize certain patterns for future predictions. This process of making content available in various formats like text, video, images, etc., recognizable to machines is called data annotation and labeling.
In this post, we will focus on video annotations, what it is, its workings, and how it benefits a company’s AI ML model.
So, what is video annotation?
Well, video annotation is the process of identifying, tagging/marking, and analyzing video data. It helps prepare datasets to train Machine Learning (ML) and Deep Learning (DL) models. In short, a combination of human annotators and automated tools examine the video and label the data as per predefined categories to compile training data for machine learning models. The most prevalent use cases of video annotation involve autonomous cars, tracking human activity, and postures for sports analytics. It is also used for face and expression recognition systems.
How does it work?
Video annotation is a part of the larger AI field of Computer Vision (CV) which seeks to train computers to mimic the perceptive qualities of the human eye. In a video annotation project, a combination of human annotators and automated tools identify and label target objects in video footage. Subsequently, an AI-powered computer processes this labeled footage through machine learning (ML) techniques and learns to identify target objects in new unlabeled videos. The more accurate the video labels, the better the AI model will perform, helping companies deploy with confidence and scale quickly.
How Video Annotation Helps Train AI ML Models
Over and above identifying and tagging objects frame-by-frame and making them recognizable to machines, which can also be achieved using image annotation, video annotation is highly valuable in building training data sets for visual perception-based AI models. Here’s how it adds value to AI ML models in deep learning:
Object Localization for Computer Vision
A video has multiple visible objects, and localization helps in identifying the primary objects in an image. Ones that are most visible and focused in the frame. Identifying the main image along with its boundaries is the chief objective of object localization.
Tracking Human Activity & Understanding Postures
Another significant value addition of video annotation involves training CV-based AI or ML models to track human activities and predict the poses. These are commonly used in areas where there is considerable human movement, like sports fields, to track athletes during contests and sporting events. Precisely labeled data that annotates the smallest of details, like the facial expressions and specific postures while performing various actions allow robots and automated machines to identify and learn about human activity and interactions in varied situations and respond to them.
Object Tracking for Autonomous Vehicles
Video annotation plays a vital role in identifying objects for autonomous vehicles. Using annotated videos, autonomous vehicles can recognize objects like street lights, pedestrians, signboards, signals, cyclists, pedestrians, other vehicles, etc., on or around the road. Advanced video annotation tools can accurately label videos frame-by-frame to help AI developers build visual perception AI models, which further helps in building a fully functional and reliable autonomous vehicle.
To sum up, precisely annotated and labeled data sets, be it in the form of annotated videos, text, or images, are the fuel that trains the algorithms that build perceptive AI and ML models. One cannot imagine AI ML without sufficient and quality data sets. Therefore, when it comes to video annotation, experience and expertise and access to state-of-the-art tools are crucial as AI programs can only function optimally with concisely labeled data.
At Amantya, we work with clients across varied sectors like automotive, retail, healthcare, and robotics to create high-quality training data sets, making it possible to integrate AI in all walks of life. Our highly specialized team of data annotators provides the best quality annotated videos using best-in-class video annotation tools for deep learning or machine learning.
Want to know more about our data annotation capabilities? Write to us at email@example.com. We will be happy to create a customized solution based on your specific business needs.