Obsah
Anotace dat videa: Co to je a jak se používá v reálném světě
Similar to the annotation of images, video is among the most important techniques that researchers are relying upon to aid machines in recognizing objects in their environment using computer vision. When working on data annotation that requires annotators to recognize moving objects with a variety of methods in order to make them identifiable to machines. The article below we’ll explore the in-depth world of video annotation and explore certain industries in which its significance is increasing and the different types of methods for data annotation and many other information.
What is Video Annotation?
Video annotation refers to the process of take a picture of each object you see in the video using frame-by-frame annotations that make the moving objects visible to machines or computers. It’s more complicated than image annotation as the object you are interested in is moving.
Another problem is typically the amount of information that needs to be annotated. Because each video clip has to be annotated frame by frame, the volume of data could rapidly increase. This is one reason why lots of companies that are developing machine learning-related projects prefer to outsource this work to an annotation service like Labelify.
What industries are increasingly relying on Video Annotation?
Video annotation is frequently employed in the automotive industry to help train machine learning algorithms that drive autonomous vehicles. This allows autonomous vehicles to identify thighs as street lights, cars, streets, pedestrians and any other objects they encounter while driving. In addition, tracking human movement in addition to pose recognition is utilized by video game developers to design the games we all enjoy. This is done by accurately noting things like expressions on their faces, as well as how they and their postures when doing various activities. In the future, we’ll give some examples in which Labelify annotations are used to create soccer and hockey games. But before we move on to that, let’s take an overview of the different kinds of annotations for data.
The types of data annotation
There are a variety of different kinds of annotation for data and the decision on which to choose will be contingent on the specific project. The most popular methods for annotation of video data are:
- Landmark annotation is where you place landmarks or points on the faces of individuals in video clips to distinguish facial features and expressions.
- Semantic segmentation: The purpose in semantic image segmentation is to mark every pixel in an image according to the classification of the image being displayed. This is among the most precise methods of data annotation.
- 3D Cuboid annotation – The data annotator will draw an arc around the object that lets the system recognize the length, width and the height.
- Cuboids are a type of polygon. Since cuboids are restricted to right angles, the polygon annotation can be useful to add additional lines as well as angles. In essence, the annotation would have to determine the parameters of the object from both sides.
- The polyline annotation technique is commonly used to mark the training data of autonomous vehicles, to ensure that they can accurately identify the road lanes and street markings. All of this needs to be tagged with polylines to enable the system to recognize lanes, as well as define the bicycle lanes, directions, divergence and opposing directions to see the surrounding area to ensure safe and secure driving.
There are a variety of scenarios or data annotation types in which the techniques mentioned above could be employed. These potential video data annotation types include:
- Video with object tracking- This is the process of notating a video with labels for objects as well as spatial locations for entities that are identified in the video video segments.
- Fragmentized into frames- Sometimes, you have to categorize the objects in any given frame that aren’t moving, in contrast to the tracking of objects mentioned previously.
- Points of action – This could include placing points to mark every motion and let the system be able to discern how motion of the objects or people in the footage.
- Labeling – This refers to making sure that all objects are labeled as well as other items that the system needs to identify.
The challenges of video Annotation
There are numerous specific issues that video annotation can bring to bear data annotation. The challenges are:
Just getting the annotation completed. One of the difficulties that video annotations pose are that these objects aren’t still and annotations must take a picture of the moving object in the screen of a computer. This is the reason why videos are typically converted into smaller clips, such as GIF files, and the specific objects are identified to be annotated.
Maintaining an extremely high level of accuracy Annotating data is an extremely tedious and monotonous task , and if an annotation isn’t completely focused on their work it is difficult to keep a high precision level.
The huge amount of data. We have to take into account the massive magnitude of the data. Because a large amount of video data for training is needed to train a machine-learning system, and the video could be further divided into sections, the volume of data required to be annotated rapidly.
Picking a service provider All of this leads us to identifying the best outsourcing service provider that can take care of all your data annotation requirements for video because it’s not effective to perform this work internally. The outsourcing service provider you choose has many data annotation experts on staff as this will allow them to start your project faster and also expand your project because the amount of data that they can manage quickly add to.
After we’ve learned about the various methods that are available, the types of techniques, and the challenges of annotation on video data and highlighting, let’s look at some of the applications.
Video annotation uses cases from Labelify
We’ve said that annotation of video data can be used to create video games. We have recently began working on some exciting projects to develop soccer and hockey games.
Video annotation and tagging of Rugby games from games on video to live sports events, each action can be tracked to allow it to be used as training data to be used in AI as well as machine-learning models within games industry. In this project we had to make annotations on hockey matches that were played live according to the specifications of the client, and to specify every single event that occurred during the game.
Video annotation and tagging to soccer games We’re working with a company who offers software to analyze the results of sports games. The project is centered around watching games and noting the games’ events like passes, outs and goals. In the course of this project, we were required to provide the time stamps for the games, as well as the name of the team, the date comment, event, and other particular aspects. An annotator team of 80 were trained for this project.
