Video Annotation For Computer Vision Training: Why You Should Start Using It Now
April 13, 2022

Video Annotation For Computer Vision Training: Why You Should Start Using It Now

Casey Chang
Casey Chang

When training AI models, most people focus on annotating JUST images and forget about the option to annotate videos (FYI, CrowdAI supports both media types). However, using properly annotated videos to train computer vision models often accelerates a company’s path to operationalizing models. Below are just a few unique benefits to simmer on to help you define your path from model to value.

1. Faster Annotation

Compared to images, 100 frames of a video can be a lot easier to annotate. Really, how you may ask?

Well, think about a collection of photographs in your 2021 scrapbook. Each picture in the book has a unique background (e.g. a selfie on a hike in the glaring sun and a candlelit dinner in low light) and was likely shot at different times in the day. The same is rarely true of a video. If we have a video of a ball rolling down a hill, a lot of elements in the background stay constant and the ball moves only so far in each frame—there’s lots of redundancy between consecutive frames, and many elements of the background stay the same. This makes annotating the object of interest in each frame relatively simple.

Because consecutive frames are very similar, it is oftentimes MUCH faster to annotate 100 frames of a video than to annotate 100 unique images. With special tools, we can even automate some of the labeling process. In one such example, we only have to annotate a few frames, and our platform automatically annotates subsequent frames for the same object (this is called linear interpolation). This speeds up the annotation process since we only have to make sure AI-generated annotations are accurate.

Comparison infographic of image data and video data with a small stack of squares representing images and a tall stack of photos representing video data.

2. Videos tell a Richer Story

Unlike static images, an object of interest is constantly in motion in videos. This makes videos extremely context-rich. For example, if you want to figure out if you are displaying the proper form in your morning jogs, a video of you running probably is more informative than a snapshot image. In this way, videos allow us to view changes in objects over a period in ways that images cannot.

Video of women running across screen
Photo of women mid run

3. Higher  Consistency and Accuracy

As mentioned above, videos allow us to use a trick called linear interpolation to speed up some of the manual work required for labeling large videos. Not only does interpolation make annotating faster, but it also results in more accurate and consistent annotations. This is because it is easier for a computer to apply consistent logic when tracking an object across multiple frames than for a human to annotate many photos consistently. Thus, video annotation can lead to more consistency and accuracy than images!


At CrowdAI, we have embedded our years of experience working on diverse AI projects across industries into a single platform experience. Recognizing the advantages of video annotation can help you make the most of our platform and plan a quick path to impact-delivering computer vision. Now, you can start your image or video annotation journey today by taking advantage of our free CrowdAI Explorer account.

Advancing AI
Understanding AI
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.