What is Video Annotation


Introduction

Video annotation, similar to picture annotation, is a method of teaching computers to recognize objects. 

Both annotation methods are part of the Computer Vision (CV) branch of Artificial Intelligence (AI), which aims to teach computers to replicate the perceptual features of the human eye.

For a video annotation project, a combination of human annotators and automated tools helps to annotate the objects in the video.

The annotated video is then processed by the AI model, with the goal of learning how to recognize target items in new, unlabeled videos using machine learning (ML) techniques.  The AI model will perform better if the video labels are accurate. 

In this article, we will learn about what is video annotation, its types, what industries are using it, and the challenges of video annotation.

But first, let’s start with…

What is Video annotation?

Video data annotation is a process by which the annotators label or annotate objects frame by frame to teach the AI how to recognize those objects in the new dataset.

The data is annotated by making use of different shapes, drawings, or comments.  This is very similar to image annotation but at the same time, far more complex than image annotation because, in an image dataset, there is less complexity, as there are fewer objects to annotate.  But video is more complex because of the presence of multiple objects and subjects like humans, vehicles, objects, and more. 

Also, in the video, the amount of data that has to be annotated is less. Whereas, in the video, the amount of data is large, as the annotator needs to label the video frame by frame. 

Which industries are relying on video annotation?

There are some industries that rely on video annotation more than others. More and more industries are making use of video annotation because it helps in time-saving, and accurate results. 

Here are some industries that make use of video annotation:

1. Automotive: The biggest use case in the automotive industry is self-driving cars. The self-driving cars need a huge amount of data in the form of video that can help the car detect people, signs, objects, zebra crossing, other cars, brakes, and more. 

Another use case in the automotive industry can be making AI find parking spots in the parking lot. The car will analyze the parking lot to find the best place to park the car.  Another use case can be AI detecting potholes and bad road conditions. 

2. Gaming: Video game companies use human activity tracking and pose estimation to create games that are highly realistic. 

This includes accurately annotating things like people's facial expressions and how they pose while performing different actions in games.

3. Medical: The major benefit of AI in the medical industry is to help doctors with the diagnosis and imaging of patients. 

Video annotation helps in analyzing mammograms, X-rays Dataset, CT scans, and more to monitor the patient’s progress. 

4. Retail: A great example of video annotation in retail is Amazon Go. Customer enters the store and adds things to their cart. With the help of sensors and cameras, the cart will calculate the total price and customers can go without checkout, as the money will be deducted from their Amazon account. How cool is that?

Another use case of video annotation is inventory management.  The cameras will tell if any item is out of stock, or misplaced. Use case f AI is to detect the mood of a customer by looking at their face. If their mood is not good during the shopping, a sales representative can assist them. 

5. Surveillance: Government agencies can use CCTV footage to detect traffic in a particular area, detect potential crimes through video annotation, and check the speed of cars. 

Similarly, companies can use video to mark the attendance of their employees. 

6. Agriculture: Through video from drones, the AI can detect different types of crops, and analyze the grain quality, weed growth, herbicide usage, and more. Video can also help the farmer in livestock management. 

7. Industrial: Video annotation is increasingly being used in the manufacturing industry to improve productivity and efficiency. 

Robots are being taught to navigate through stationary, inspect assembly lines, and track packages in logistics using annotated videos. Robots using annotated videos are assisting in the detection of defective items in production lines.

What are the types of video annotation?

The type of video annotation largely depends on the type of project you are developing. Some projects may require you to draw bounding boxes, polygons and more. 

Here are some of the common types of video annotation:

3D cuboids: 3D cuboids are like bounding boxes, but in bounding boxes, location, length and width are measured. In 3D cuboids, the location, length, width, and depth are also measured. Depth is an added factor here. 

This becomes useful when AI wants to determine the length, height, and width of an object. 

Image classification: According to Google developers, “Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labelled example photos.”

The best example of this is the grouping of different subjects in Google photos. You may have noticed that Google groups things like different people, places, animals and more. These are the places where image classification comes into play. 

Polygon annotation: Polygonal segmentation is used to identify the shape and location with the highest accuracy because it maps out the anchors' pixel by pixel. 

Basically, polygon annotation is useful when there are more angles to a subject.  The annotator must define the parameters surrounding the object from all sides.

Semantic segmentation: This is very useful for self-driving cars as they have to know everything in their surrounding area. And in semantic segmentation, everything is given a label, and it is done pixel by pixel. 

What are the challenges of Video annotation?

There are many challenges that come with doing annotation of videos, as the amount of data is large. Here are some challenges that come with video annotation:

1. The amount of data is huge: One of the difficulties with video data annotation is that the objects are not still and the annotators must capture the moving object on the computer screen.  This is why videos are typically converted into smaller clips, such as GIF files, and individual objects are then identified for annotation.

Then there's the issue of the dataset size. Because a machine learning system requires a large amount of video training data, and this data is further broken down into segments, the amount of data that needs to be annotated grows quickly. 

2. Accuracy is hard to maintain: Data annotation is a time-consuming and monotonous task, and maintaining a high level of accuracy can be difficult if the data annotator is not completely focused on their job.

3. Choosing a great service provider: All of this leads us to the need to find the right outsourcing partner to handle all of your video data annotation requirements, as doing so in-house would be inefficient. 

Because the amount of data can quickly add up, the outsourcing provider you choose should already have a large number of data annotators on staff. This will allow them to launch and scale your project faster.

How GTS  can help you in Video annotation?

When it comes to video datasets, video data collection, and video annotation, we at Global Technical Solutions have the expertise, knowledge, resources, and capacity to provide you with everything you need. Our team Provides the highest quality datasets and are tailored to your specific needs and problems.

We have members in our team who have the right knowledge, skills, expertise and qualifications to collect and deliver video data for any situation, technology, or application. Our multiple verification systems consistently ensure that we deliver the highest quality videos.

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