Machine learning and artificial intelligence rely heavily on the annotation of videos. Training data can be generated by tagging video clips and their information. A computer vision model is trained to identify and detect objects using this data. Annotating video and images are fundamentally similar operations. Each frame in a video serves as a snapshot of the video. It then becomes necessary to annotate each frame in the same way.

What are the most often used types of video annotations?

When searching for annotation solutions of videos, you may come through these most common video-annotation types. Understanding each type can assist you in determining when and where to employ it in your business.

2D bounding boxes

To identify, classify, and categorize objects, rectangular boxes are created. Annotators must manually draw boxes around the object to be annotated. Using a variety of frames, these boxes are drawn in real-time. Annotators should draw the boxes as close to the item’s edges as possible to create the most accurate portrayal of the object. Later annotators identify the object’s class and characteristics.

When is this method appropriate?

For item detection and localization, such as automobiles, humans, etc., 2D bounding boxes can be employed.

3D bounding boxes

This sort of video-annotation, like 2D bounding boxes, is used to create a more realistic 3D representation of a given object. 3D bounding boxes provide more precise answers since they allow you to determine an object’s width, length, and depth even when moving.

When is this method appropriate?

Bounding boxes in 3D polygons are also utilized for item identification, classification, and location. 3D bounding boxes, on the other hand, are more useful and give you a more accurate sense of objects than 2D bounding boxes.

Splines and lines Annotation

Many companies in the field of self-driving cars employ this form of video annotation. Robots can recognize borders and lanes using splines and lines in this manner. AI algorithms may quickly distinguish between frames using video annotators by drawing lines between locations.

When is this strategy appropriate?

Using spines and lines as well as autonomous vehicles, warehouse robots can be taught to detect and distinguish between various conveyor belt components.

Key-points and landmarks

Key points and landmarks are commonly used to distinguish even the tiniest shapes, postures, or objects. As a result of this technique, many dots appear throughout the image. This skeleton is then linked across all frames, key points, and landmarks to build the object’s skeleton.

When is this strategy appropriate?

Detecting facial traits, face recognition, bodily parts, postures, etc., may all be done using this technology.

Polygons

It is common for polygons to be utilized when 3D or 2D bounding boxes cannot accurately describe an object’s motion. Produce and link dots around the object’s outer perimeter to create lines under this approach. An experienced and skilled annotator, on the other hand, is needed to use the polygons approach to its full potential. Hence, it is best to take help from an experienced data annotation company.

When is this method appropriate?

Sometimes the polygon approach is useful for items that don’t perfectly fit in a bounding box. In aerial videography, these can be used to capture a variety of shapes, including bikes, buildings, and residences.

Semantic segmentation

In this process, videos are broken down into smaller parts and then annotated. In order to do this, professionals in computer vision must spend a lot of time analyzing video frames and determining what each pixel means for each object.

When is this method appropriate?

Using this method, you can distinguish exactly between items like roads, cyclists, buildings, etc. It may also be utilized for specific anatomy and body part labeling tasks.

A large amount of data is needed to teach computer vision models, which makes it challenging for in-house groups to scale and produce correct results. You may quickly receive high-quality and accurate results by outsourcing video-annotation services to a company like this. Annotating a video can be tough if you don’t access the necessary information and tools. As a result, this time-consuming and labor-intensive process might detract from your company’s essential business activities.

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