Many people unfamiliar with the technologies assume that artificial intelligence and machine learning can perform complex tasks from the jump. But the reality is a little different.

AI models require training to perform the tasks users want accurately. While some models can have high efficiency quickly, anything related to video needs to process a mountain of data before making accurate predictions. To do that, it needs video labeling.

What is Video Labeling?

Labeling is a simple concept. The goal is to feed machine learning models annotated data so that it learns how to distinguish objects in the video it views. Computer vision data annotation services simplify this process using automation, but it's critical to preparing AI for deployment.

Precise labeling and complex algorithms allow AI models to perceive a broad range of objects in video. It's what educates the model and helps to facilitate many functions.

What Video Labeling Can Do

Beyond training AI models, labeling helps systems distinguish objects frame by frame. With that capability, AI can use labeling to perform the following functions.

Object Detection

Detection is an essential function of video-based. These models will view video content frame by frame, and labeling helps them spot target objects as they enter the scene. Computer vision data annotation services ensure that the models correctly identify targets.

Depending on the purpose of the model, detection may trigger the system to perform other tasks, notify users of its presence and more.

Localization

Another critical function AI models need when analyzing video is localization. In many cases, videos contain multiple objects. Take a security feed at a high-traffic transportation hub as an example. AI must distinguish each target object, determine where it is in the frame and put it into focus.

Object Tracking

Labeling also helps to track objects. After detection and localization, tracking allows AI to follow the target as it moves throughout the video. Models can also learn to track activities, actions and poses in more complex deployments. The technology serves many purposes and can benefit companies that use AI for security, surveillance, field sports, art and more.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at automated annotation blog.