- Efficiency & Speed
Let’s drill down into the first quality - flexibility.
Why is flexibility important?
Let’s back up for a moment. Computer vision (CV) is a field of machine learning in which algorithms are trained to learn how to “see.” The goal is for computers to be able to visually interpret images and videos just like we do. This means the computer would view an image and be able to:
- Recognize the objects in the image.
- Verify if “X” object is in the photograph at all.
- Detect where the objects are in the image.
- Segment which pixels belong to the object in the image.
- Classify which broad category this object belongs to.
- Identify what type of object it is.
This is pretty simple for a human, but it is incredibly complex for a computer and requires a huge volume of labeled data from which to learn. It becomes even more complex with video, because the objects are moving in a 2-dimensional depiction of 3-dimensional space. Puzzling through these issues is worth it though because there is enormous potential for numerous applications including retail (e.g., automated checkouts), medical imaging, self driving cars, etc.
Flexibility addresses two central challenges to video annotation - volume and complexity.
Scaling for Volume
ML projects typically require a large volume of training data to achieve the desired level of model confidence. CV video projects are on the higher end of the volume spectrum. Consider this: the standard frame rate for film is 24 frames per second, 1440 frames per minute, and 86,400 per hour. That’s a lot of images! Just think of the number of hours required for a self driving car to learn to navigate the complexity of a city street. Even if only a fraction of those frames need labeling, human judgment cannot achieve this scale without augmentation via AI enhancement technology.
Every unique CV use case requires training data that is configured to meet the project’s specific needs. A video annotation technology platform must be nimble enough to classify individual frames as well as label any action occurring among or between various target objects. And labeling CV training data requires an array of tools including keypoint, polygon, bounding box, object detection and classification, parts ID and landmark detection, instance and semantic segmentation, as well as actions and interaction identification.
So, that’s why flexibility is important
To address both volume and complexity, a computer vision labeling and annotation platform must be flexible and versatile enough to configure and implement the sophisticated workflow processes that are required to meet the high levels of quality needed to train algorithms.
Read more about the 5 Questions to Ask for Successful Video Annotation
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