Computer vision (CV) was formerly focused on identifying and classifying information from still images but has now evolved to respond to complex video data.
Video annotation has emerged as a critical component for developing AI applications that understand and respond to visual data in motion.
In this article, we will explore the concept of video annotation, its applications, and the challenges of this complex form of data annotation.
Video annotation is the process of labeling or tagging specific objects, activities, or events within video frames to provide meaning to raw visual motion data.
By providing supervised models with annotated data, they can learn from the labeled data, recognize patterns, and make predictions when encountering new, unannotated video data.
It plays the same role as data annotation for other computer vision (CV) tasks.
Here are the applications of video annotation and the types of models and use cases it supports:
Video annotation plays a crucial role in training AI models for a vast range of applications across multiple sectors and industries:
Video annotation is inherently complex as video data has many frames, so it's similar to image annotation but vastly more intensive.
Here are some of the challenges of video annotation:
Video annotation is complex and presents many challenges for the annotation task itself:
Primarily, video annotation is labor-intensive. For example, labeling complex, long videos requires thousands or even millions of labels. Plus, the data is storage-hungry, which drains the resources of data centers.
There are many ML models. Here are 5 of the most popular and widely used:
So, how do you actually go about labeling video data?
The first step involves selecting and preparing the video dataset that will be annotated.
This includes determining the length of the video, selecting relevant video clips or sequences, and deciding on the annotation techniques to be used based on the specific goals of the AI project.
Video data consists of a series of individual frames (images) displayed at a certain frame rate.
Before annotation, the video must be broken down into its constituent frames.
Depending on the requirements of the AI model, it may not be necessary to annotate every frame. Instead, annotators may work with keyframes containing significant changes or information compared to the preceding frame.
Different annotation techniques can be applied to video frames depending on the desired output and the complexity of the data.
Common annotation techniques for video data include:
The actual labeling process can be done manually, where human annotators label the frames using annotation tools or through automated methods that employ computer vision algorithms to detect and label objects.
Often, a combination of manual and automated techniques is used to improve accuracy and efficiency. This is particularly important for large quantities of video data, which are exceptionally labor-intensive to label by hand.
Human annotators typically review and refine the automatically generated labels to ensure quality and consistency, acting as a human in the loop.
After the annotation process is complete, performing quality assurance checks to ensure the labels are accurate, consistent, and meet the project requirements is essential.
This step may involve reviewing a sample of the annotated data, checking for errors or inconsistencies, and providing feedback to annotators for subsequent label batches.
Once the labeling process is complete and quality checks have been performed, the annotated video data is aggregated and formatted for feeding into supervised ML models.
This may involve combining the annotated frames back into a video sequence or converting the annotations into a format that machine learning frameworks can easily ingest.
Video annotation is a critical aspect of training cutting-edge AI models.
It’s used for everything from training driverless vehicles to alternate and virtual reality and robotics, where robots are trained to understand complex sequences – an ability humans take for granted.
Here are a few interesting case studies where video annotation has been effectively used for real-life AI applications.
Waymo is one of the leading companies in the development of self-driving cars. Their AI system relies heavily on annotated video data to understand and navigate real-world driving scenarios.
In one of their projects, they used video annotation to label objects such as other vehicles, pedestrians, cyclists, and road signs in their training data.
This data was then used to train their AI models to recognize these objects in real-time while driving. The quality and accuracy of these video annotations are critical to the safety and reliability of their self-driving technology.
Second Spectrum is an AI company that uses machine learning and computer vision to provide advanced sports analytics.
They use video annotation to train their AI models to track players, understand game strategies, and provide real-time insights during games.
In a partnership with the National Basketball Association (NBA), Second Spectrum annotated thousands of hours of game footage, labeling player movements, ball possession, and game events.
The annotated data was used to train their AI system, which now provides real-time analytics during NBA games.
Deep Sentinel is a company that offers AI-powered home security systems. They use video annotation to train their AI to detect potential security threats accurately.
In a case study shared by the company, they annotated various video feeds to label potential threat scenarios like break-ins, vandalism, or suspicious behavior.
The annotated videos were then used to train their AI system, which can accurately differentiate between non-threatening activities (like a cat wandering into the yard) and actual security threats.
This is just a minuscule selection of video annotation’s uses and applications with computer vision and AI/ML as a whole.
Video annotation for AI refers to the process of labeling or tagging elements within video data.
This can involve marking out objects, people, actions, or events frame by frame and assigning them relevant labels or categories.
The goal is to create a rich dataset that can be used to train AI models, specifically in tasks like object detection, activity recognition, and video classification.
Video annotation is complex and supports a vast range of complex AI models across numerous industries and sectors.