Similar to image annotation, you can have video annotation that can aid in computer vision. In video annotation, the annotators have to detect all the moving objects using several techniques to ensure that the machines can recognize them. There are many applications of annotated data, and while the task needs human intervention, they also use a video annotation platform that helps ensure the quality and accuracy of the annotation.
Video annotation, defined
Video annotation is the process of capturing every object in a video. In every frame, annotated lines make the moving objects recognizable to machines or computers. The process is more difficult because things are in motion.
The task is more challenging considering that the amount of data grows frame-by-frame. Thus, many machine learning companies either outsource the project or invest in a video annotation platform.
Business applications of video annotation
The automotive sector is one of the heavy users of video annotation because the industry uses it to train machine learning algorithms as they continue to develop driverless vehicles. For example, video annotation teaches self-driving cars to recognize and differentiate between objects, such as street signs, pedestrians, other vehicles, street lights, and other items the vehicle may face on the road.
Video game companies use video annotation to track pose estimation and human activity that help them create better games. Unfortunately, annotating videos for video games is tedious because it involves accurate annotation on various things, such as facial expressions and how they pose when doing different actions.
Types of annotation
An annotator’s job requires precision and attention to detail. Aside from learning the basics of data annotation, they also have to know the different data annotation types. Some projects demand a specific type of annotation or even a combination of various kinds.
- Landmark annotationrequires the placing of landmarks or points on the faces of people shown in videos. The points help in identifying human expressions and facial features.
- Semantic segmentation requires highly detailed work. This type involves adding a label to every pixel of an image, with a corresponding class of what each represents. It can apply to scene understanding, localization, robotic navigation, and autonomous driving. For example, semantic segmentation helps detect bridges, roads, and other things from satellite imagery. In medical imaging analysis, it can be used for cancer detection.
- 3D cuboid annotationmeans drawing cubes around an object, which lets the system recognize an object’s height, width, and length.
- Polyline annotationis often used to label training datasets for self-driving vehicles to identify road markings and street lanes. The annotation is precise so that the cars can determine different lanes, opposite direction traffic, divergence, directions, and bicycle lanes.
- Polygon annotationis used for defining angles and lines that are difficult to accomplish by using cuboids. In addition, polygon annotation allows the definition of the parameters around the object’s sides.
The above annotation types are often used for data annotation, while various kinds are specifically used for video annotation, such as:
- Video with object tracking adds labels to spatial locations of entities and things found in the video.
- Broken into framesare used to label the static objects in a given frame.
- Points of action annotation places points to label all the motions captured in the video frame. Thus, it allows the system to comprehend how the objects or people in the frame move.
- Labeling adds tags or labels to every object and other things of interest that the system must recognize.
Video annotation challenges
By now, you can understand how demanding data and video annotation can be. Thus, it is not difficult to imagine that the task faces many challenges. Therefore, it is vital to know the requirements of the job and the issues that can affect the completion of a video annotation project.
In video annotation, the entire film is often converted into shorter clips. This method helps the video annotation to capture the moving objects precisely on the computer monitor. The task requires focus and dedication because it demands accuracy. If you look at the job objectively, you will find it monotonous and boring. Thus, if the person is not committed to the rigors of the task, the result will be useless or erroneous.
Training a machine learning system involves a considerable amount of datasets. Each video file needs to be divided into smaller segments that are easier to handle. Therefore, you can expect that the datasets for annotation will multiply.
In video annotation, every action (even inaction) must be labeled so that the information can be used as training data for machine learning and AI models by the industries that need them. You will understand how demanding the task is if you know that annotating a full-time soccer game may require more than 100 annotators.