Our Complete Guide to Video Annotation

Explanation of real-world video annotation uses.

Our Complete Guide to Video Annotation

Video Annotation Explained

Video annotation or video labeling is the process of adding annotations to videos. The main purpose of video annotation is to make it easier for computers that utilize AI-powered algorithms to identify objects in videos. Properly annotated videos create a high-quality reference database that can be used by computer vision-enabled systems to accurately identify objects such as cars, people, and animals. With an ever-increasing number of everyday tasks becoming reliant on computer vision, the importance of video annotation cannot be overemphasized.

What are the Different Types of Methods of Video Annotation?

Several different video annotation methods exist. The right method needed for adding labels during a specific annotation project depends on the type of video that is being annotated and what the annotated data will be used for. Some annotation methods include:

Bounding Boxes

Bounding boxes refer to a method of video annotation in which annotators draw a box around a specific object or image in a video. Annotation is then added to the box so that computer vision tools can automatically identify similar objects when they appear in videos. This is one of the most frequently used methods of video annotation.

Polygon Annotation

Although polygon annotation is similar to bounding box annotation, polygon annotation can be used to identify more complex objects. Polygon annotation can be used to annotate any object, regardless of its shape. This form of video annotation is well suited to objects with abstract shapes, such as houses.

Semantic Segmentation

Semantic segmentation is a form of video labeling in which objects are separated into their component parts by a person, referred to as an annotator. Annotators can also work as a team when dealing with multiple videos, resulting in quicker processing times and high-quality output. These component parts are then annotated or labeled individually so that computer vision-enabled systems can recognize specific components that make up a unit easily.

Key Point Annotation

This type of annotation outlines the key points of a specific shape. Key point annotation is very versatile and can be used with a variety of shapes, including the human face. By highlighting the outline of a specific object, key point annotation makes it possible for computer vision systems to perform the classification of objects based on key landmarks.

Landmark Annotation

Landmark annotation is very similar to key point annotation in that it relies on points with a label, also known as a landmark, to identify objects in video frames. This type of annotation is very suitable for use with computer vision systems that are designed to detect objects like the human face. Landmark annotation also works well for use in the training of computer vision systems because this form of annotation can produce very accurate results.

3D Cuboid Annotation

Polyline annotation is mainly used for AI or computer vision training purposes. Through polyline annotation, specific areas can be cordoned off so that computer vision systems only operate within a set perimeter.

Rapid Annotation

Rapid annotation can be used to quickly annotate large amounts of video based on specific project parameters. Rapid annotation is mainly suited to computer vision training projects and the rapidly generated labels can be used to train systems effectively and quickly. Rapid annotation can analyze and label many individual images very quickly.

In which Industries or Sectors is Video Annotation Mostly Used?

Almost all modern businesses or industries can make use of video annotation in one way or another. As more and more of the systems we rely on become powered by AI, the list of applications for video annotation will continue to expand. While the specific annotation technique that is used will vary from sector to sector, in general, all industries can benefit from annotation. Some of the sectors which are already making use of video annotation are:


A good example of video annotation in the medical sector is the ability of computer vision to help medical practitioners and scientists identify objects seen under a microscope. Computer vision can be used to identify specific cell types and other biological elements accurately, which can help both patients and doctors.


Autonomous vehicle technology is one area in which computer vision and video annotation are used extensively. Computer vision makes it possible for vehicles to monitor their surroundings and make decisions based on this information. Without the use of video annotation, the creation and operation of autonomous vehicles would not be possible.

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Architecture and Geospatial Applications

Video annotation can be used to train computer vision algorithms to identify specific objects such as entire buildings and/or the different levels or wings that make up a building. This form of computer vision has many different uses, but it is especially prominent in the security industry.

Traffic Management

Computer vision is especially helpful in the management of traffic and video annotation can be used to train AI algorithms to identify features such as vehicle number plates. In this way, computer vision can analyze a video stream frame by frame to identify specific vehicles in traffic so that processes like toll collection, fining and congestion management can be automated.


Video annotation can be used in various production processes. Some examples are monitoring production lines, assessing products or components for correctness, and identifying areas in which productivity can be improved.


Video annotation is used in the retail sector to analyze the behavior of clients in a store. Computer vision can be used to identify patterns and traits and gives retailers insight into their customers. This in turn shows retailers where and how they can optimize their bottom line.

The Video Annotation Process

Video annotation projects can often be time-consuming and complicated, which is why they work best when a fixed pattern or structure is used. It is very important to clearly define the objectives of any video annotation project before the project commences. In many cases, it might be a good idea to outsource video annotation work to an agency that specializes in computer vision training. Agencies typically follow a set process to achieve the best possible outcomes.


The process almost always starts off with an initial consultation during which the basic parameters for the project are established. After this, the agency will carefully evaluate all the available data and suggest the best video annotation approach. Once consensus is reached, the actual data annotation process can begin. This is followed by an evaluation of the project to ensure that the desired outcomes have been achieved. Using an agency to assist with video annotation projects has many advantages, including the creation of high-quality training data that lays a solid foundation for further development. In addition, using an agency can often be more cost-effective than using in-house staff. 

Another way to perform video annotation is by using a video annotation platform. By using a video annotation platform, it is possible to achieve the same excellent results but at a much lower cost.  

Whether or not an agency is used, it is critical that video annotation projects are planned carefully and those clear outcomes are established before the project commences. By doing this, success is much more likely.


Video annotation is an important tool that has many advantages for industry and society. While video annotation has already been adopted in many industries, the list of applications for this technology is growing continuously, and more uses for computer vision are discovered on a regular basis. Because video annotation is such an important part of modern technology-driven systems, it is important that enough attention is paid to the further development of computer vision through video annotation.

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