The video annotation process could be described as the process that makes it easier for machines to learn how to recognize the objects in a video, understand the context, and follow the desirable target(s) through every sequence of it. It is similar to the image annotation process, with the main difference seen through sequenced 3D data in a real-time manner.

There are 2 types of Video annotation methods:

Single Image annotation – Before automated tools became reality, the video annotation process was a low efficient and effortless process. For example, in 30 frames per second(fps) there would be 1800 frames in a minute. We can all agree that annotating it was a huge time and money waste with high-level error possibilities.

Continuous Frame method– Automating this process enables easy recognition and constant tracking of objects and their locations frame by frame, with the preserved flow of information, gathered. So, it became possible to identify the desired object in the first sequence of the video, which disappears for the next few frames and reappears again.

This option of video processing has been shown as highly important and desirable in finding missing persons. Most data scientists are processing video streams frame-by-frame in real-time, and label each video sequence using image classification model methods. That is one of the main reasons why most Deep Learning and Machine learning development contributors choose to annotate a specific frame and repeat the process after substantial numbers of frames have perished. One of the main challenges of video annotation is leveling up the resolution of the videos, so objects can be more easily identified and tracked. Engineers around the world are working on developing tools for optical flow in order to leverage contexts of object identification across different frames. We can use a previously mentioned example of finding missing people- video capture from city video surveillance can be made in a very low resolution, and it’s hard to rely on as valid evidence (person’s face image, in this case). With the development of higher camera resolution, that kind of dilemma would be overrun.