Video Annotation: What am I looking at?

Last Updated June 3, 2022

video annotation

Video annotation helps make objects recognizable to machines. It’s an important task that’s changing our world, including how people shop and drive.

The global data annotation market is projected to grow from $630 million in 2021 to over $3 billion by 2028. Video annotation accounts for a large portion of this market.

By that we mean the process of teaching computers to recognize objects in video data.

And video annotation is changing many aspects of daily life. The way we drive and shop is evolving, as is the way surgeons perform their procedures.

In this article, we’re going to look at the basics of video annotation. So, hit pause on whatever else you’re doing and read on.

What is video annotation?

Video annotation is the process of adding comments, shape outlines, or diagrams on top of a video frame or frame range.

It’s like speech data annotation in a lot of ways. We define that as the human-guided categorization and labeling of raw audio data to make it more usable for machine learning or Artificial Intelligence applications.

However, instead of speech data, we’re talking about video. It’s basically the same process of categorization and labeling.

With video annotation, the task is accomplished with a mix of a team of human annotators and automated video annotation tools.

Labels are added to objects in a video clip. Computers, taught via machine learning (ML), then process these labels and identify similar target objects in other videos without the help of labeled input.

The more accurate the original video labels, the better the AI model will perform.

Let’s now look at the main approaches.

Types of Video Annotation

As we have already established, a combination of human annotators and automated tools label target objects in video footage.

Video, though, is a subset of individual frames. You either annotate frame by frame or by continuous frame.

Single Image Video Annotation

As the name suggests, you take things frame by frame.

In this process, annotators extract and label each frame individually. It’s time consuming and costly, but effective for short videos where objects are moving less dynamically.

It opens the door for potential errors, though. An object can be classified as A in one frame and B in another.

In other words, this method is a bit outdated because of machine learning and AI.

Continuous Frame Video Annotation

Here, the annotator uses tools to label objects as the video streams.

The AI has been trained through hands-on human annotation and input. As a result, it automatically tracks objects and their locations frame-by-frame in real time.

Furthermore, these tools ensure continuity. They accurately identify objects present at the beginning of the video, but vanish for several frames and return later.

This is much quicker and more effective, especially when the data volume is large. The labeling is done more precisely and consistently.

The multi-frame method has become more common as data annotation tools become popular. Still, human annotators are required assure quality and uphold the highest levels of accuracy. That’s important when the quality of the video isn’t great.

Surveillance camera or traffic footage, for example, can still be difficult to annotate based on low resolution feeds.

Video Annotation Use Cases and Examples

Automation tools and human annotators perform the task for a variety of use cases. Here are a few of the big ones.

Let’s Go to the Mall

Video annotation improves retail AI systems that monitor how customers react to products.

Annotation can help track what’s flying off the shelf. But what are the customer’s interactions with the product prior to purchase? How long do they hold it? Do they read the box? Do they ask questions?

Store managers also make product placement decisions based on video annotation. They pinpoint where customers look and where they walk to the most in the store.

Additionally, retailers curb shoplifting and theft through product recognition. Security is notified if products are grabbed but not scanned at self-checkout counters, for example.

Here’s an example of video data annotation in a grocery store:

On a related note, law enforcement annotates video for redaction purposes. People are blurred through facial recognition and other sensitive information is removed from CCTV footage to protect people’s identities.

Let’s Drive

Video annotation is prevalent in autonomous vehicles. It identifies objects on the street and other vehicles on the road or in a parking lot.

Companies also use video annotation to monitor unsafe driving behavior or the driver’s condition.

Tesla’s autopilot system, for example, is based on video annotation and computer vision.

Developers train autonomous cars with as much annotated driving data as possible. This teaches it proper driving behavior and how to react to real-time driving situations.

It’s like a driver’s education class that takes you through every possible scenario behind the wheel, with perfectly labeled examples.

Furthermore, video annotation helps monitor traffic to improve flow.

City planners locate trouble spots in terms of traffic jams, and traffic surveillance systems monitor accidents and quickly alert authorities.

Let’s Get (a) Physical

Who says there’s no heart in data? Annotation of surgical video has been a longstanding practice in medical education.

The goal of these annotations, historically, has been to provide structured, formative feedback to surgeons.

How? In a recent journal article called “Challenges in surgical video annotation,” it’s noted that the Objective Structured Assessment of Technical Skills (OSATS) is perhaps the most reported assessment tool used in surgery. It provides a global rating scale for assessment of surgical skill.

Specialized annotation teams label critical structures in millions of frames of surgical videos. They measure several aspects of surgery.

That includes ‘respect for tissue,’ ‘instrument handling’, and ‘flow of operation’. They rate them on a 5-point Likert scale. 1 mean poor performance, 3 is acceptable, and 5 mean superior performance.

Therefore, video annotation is helping make surgeons better, and procedures safer.

There may come a day where medical video annotation will train an algorithm to perform automated procedures.

For now, though, it’s mostly a teaching tool for students and a means for assessing procedures performed by surgeons.

We’re Here for Your Video Annotation Needs

Video annotation requires the best automation tools and human annotators.

Summa Linguae Technologies has the people and processes in place to help.

We can also collect the data you need for your AI.

Contact us today.

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