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Quick Guide to Video Annotation Tools and Types in 2024

Quick Guide to Video Annotation Tools and Types in 2024Quick Guide to Video Annotation Tools and Types in 2024

Video annotation tools make it possible to annotate difficult and complicated videos with higher accuracy, precision, and consistency. The global market for data annotation tools is projected to surpass $3 billion by 2028.

This article explores the types of video annotation tools and how to choose the best one for your business.

Types of video annotation tools

Before choosing the most suitable annotation tool for your business needs, you need to know the types of video annotation techniques. This will give you an understanding of your annotation requirements.

  • 2D boxes: In this type of video annotation, square and rectangular boxes are used to mark objects in the videos. Annotators draw boxes close to the object of interest.
an image showing how 2D box labels are used to tag objects on a street
  • 3D boxes: As the name suggests, this method uses 3D cuboid boxes to label the objects allowing the AI model to accurately measure all 3 dimensions of the object and its interaction with surrounding objects.
An image showing how 3D boxes are used to tag vehicles on a road
  • Polygon Labelling: When the object of interest has an irregular shape, polygon labeling provides more precision. This requires the annotator to know precision labeling.
Using the polygon labeling method to add detailed tags to pedestrians on a junction.
  • Landmarks / Keypoints: Keypoint labeling is done by adding points to the objects. This is useful for capturing the movement of facial expressions, body parts, and other moving skeletal objects. 
Using the landmarks and keypoints method to tag a picture of 2 people
  • Lines & Splines: One of the main purposes of lines and splines is to identify lanes and boundaries of an area that are popularly used in autonomous vehicle systems.
Using lines & splines tags to label a road for an autonomous driving system

How to approach video annotation

Before deciding on which video annotation tool to choose or whether to outsource the service entirely, take the following steps:

1. Recognize the need

Before choosing your video annotation tool, you need to clearly understand what value you want to achieve from it. Why do you wish to annotate your video data? If you wish to use video annotation for a long-term project, then acquiring a dedicated tool and upgrading your workforce would be a better option. On the other hand, if you have short-term goals, such as using a supermarket surveillance system, then outsourcing would be a more economical choice. 

2. The selection criteria

For outsourcing the service, you need to consider technical aspects as well as financial aspects. A video annotation tool should be considered:

  • Based on efficiency: The tool should have a user-friendly interface; it should also have hotkeys and other features which increase annotation efficiency
  • Based on functionality: The video annotation tool should be selected based on the types of labeling you require. Considering the previous section on the types of video annotation and the nature of the project are important factors in choosing the right tool,
  • Based on formatting: Video annotation can be done in various formats, including COC JSON, Pascal Voc XML, Ternsorflow TFRecord, etc. Having a tool that directly converts to various formats can be more efficient.
  • Based on application: Consider whether your project requires a web-based or an offline application. Offline tools are better for private or confidential data since it is risky to upload that data to third-party tools. 

3. Thoroughly evaluate

The next step is to search and evaluate available vendors and tools on the market. It is important to ensure that the vendor provides the flexibility and post-purchase services that align with your annotation needs. 

You can also check out our sortable and filterable video annotation tools and data annotation services lists.

Further reading

If you have further questions please do not hesitate to contact us:

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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