Video Annotation for Machine Learning / Video Labeling

Video Annotation / Video Labeling for AI Algorithms

Artificial intelligence recognizes patterns in text copy, images and videos. When for example more and more videos are being uploaded to online portals, the need for efficient monitoring and classification grows. Today the labeling of videos is mostly automated. Precisely because video data is more complex than copy and unmoving images, the demands on machine learning are correspondingly greater.

There are basically two different strategies for teaching a program the classification or annotation of video data:

• For the monitored classification of incoming data, videos are tagged in advance. For example, a video depicts a moving car or not. This information is provided to the program together with the data in ongoing training.
• Unmonitored classification trains computer programs in video annotation / video labeling by using segmentation or clustering algorithms. The program recognizes differences and similarities in a multitude of data samples.

Creation and Annotation / Labeling of AI Training Data Sets

High-quality AI training data for machine learning fulfills all requirements for a specific learning objective. The quality of the results reflects the quality of the training data, specifically the performance of trained AI algorithms.

• With video annotation / video labeling, for example, crowd workers work on a large number of videos based on concrete guidelines and label / annotate these videos for AI algorithm training purposes.
• Conversely crowd workers all over the world label / annotate existing videos, so that they can be used as datasets for monitored or reinforced learning.

The benefit of automatic video recognition is evident. Artificial intelligence – trained with annotated videos / labeled videos – optimizes video monitoring. In this way, for example, a fire, panic breaking out in a mass of people, or unusual vehicle movement can be recognized in seconds. But machine learning is also useful for labeling more nuanced video features like sentiment.

Video annotation for machine learning in the World of AI

While video annotation is useful for detecting and recognizing objects, its primary purpose is to create training data sets. When it comes to video annotation, there are several different steps that apply.

1. Frame-by-frame detection – With frame-by-frame detection, individual items of interest are highlighted and categorized. By capturing specific objects, detection with ML algorithms can be improved.
2. Object localization – object localization helps to identify specific images within a defined boundary. This helps algorithms find and locate the primary object in an image.
3. Object tracking – often used with autonomous vehicles, object tracking helps detect street lights, signage, pedestrians, and more to improve road safety.
4. Individual tracking – similar to object tracking, individual trackings is focused on humans and how they move. Video annotation at sporting facilities help ML algorithms understand human movement in different situations.