VIdeo annotation is the labelling of objects in video clips to allow machines to detect and recognize these objects. This article will explain the uses, types and methods of video annotation available
What do self-driving cars, facial recognition technology and sports-based video games have in common? They all run on AIs which rely on video annotation to perform seamlessly.
Video annotation can be described as the process of identifying and tagging objects within a video frame. The data is used for computer training for AI models which allows them to accurately recognize moving objects within a video. All this is done through deep learning- that is, a layered neural network which allows AI to learn from large swathes of data.
Good quality video annotation should be able to generate a ‘ground truth’ dataset, which is optimal in deep learning as well as machine learning. The applications of such high quality video annotations are endless, from self driving cars to the field of medicine, and many more uses are discussed below:
Video annotation can be used to locate the main subject in a video. This is usually the object which is focused within the frame. It comes in handy when there are multiple objects within a frame
One other application is to track various categories of objects, after successfully recognising them. This is most useful in self-driving cars, and enables the AI models to recognize pedestrians, cyclists and other cars. Autonomous drones also take advantage of this feature.
This application is useful in sports analysis. The AI model is trained to track the poses and actions of sportsmen and women and even predict movements
The AI is able to determine if objects in frame are in the correct positioning or have an external defect. This is useful for quality control in factory settings, like food processing plants.
There are two main methods of video annotation, the single image technique and the continuous frame technique.
This is the traditional type of technique, where each frame is examined and every object tagged, one after the other. It’s as effort intensive as it sounds, and works best with projects that will be crowd sourced or outsourced. Issues to consider include duration, project costs and errors in the final products.
In this technique, the process is streamlined through the use of methods like Optical Flow. The computer analyzes pixels in the frames before and after the current one, and through pixel motion predictions can automatically track each object as it moves from frame to frame.
This method eliminates human bias, however it is dependent on the quality and resolution of the video under review.
In this type of annotation, a 2D rectangle is created around the object to be annotated. Each box is manually drawn and must precisely enclose the object's dimensions. The object is then then labelled with it’s class (for example, car, bicycle etc) and characteristics (for example, colour and size)
It is much like the 2D bounding box, however in this case, a 3D cube is created around the object. This factors in the length, the breadth and depth of the object as it moves from frame to frame, and depicts how it interacts with the environment
Sometimes 2D or 3D bounding boxes cannot accurately capture the dimensions of an object in frame. In such a case, a polygon would be a much better method, giving a higher degree of precision. Tiny dots are placed around the edges of each object to create lines to capture the shape of the object correctly
Landmark annotations track specific parts of an object, by generating focal points or dots and linking them to build a kind of blueprint of the image. It is commonly used in facial recognition software and in identifying minute expressions, shapes and objects
Lines are used to indicate locations that the AI models have to recognise across all frames. In the field of autonomous vehicles, this data helps the computer to recognize different types of road lanes and markings.
If you’re ready to take the plunge, there are two main routes you can take to fulfil your video annotation needs. There are many free open source video annotation tools available on the web. They may come as standalone downloadable programs that can be run on your computer’s operating system, or on any modern web browser. A popular example is the Computer Vision Annotation Tool, or CVAT.
Considering the extent and parameters of your project, it might be better to consider outsourcing to a professional annotation platform. This option is usually faster and more cost effective. Professional platforms have teams of dedicated managers, quality assurance personnel and in many cases, in house video annotation tools.
Experience and skill matter when it comes to finding the right method for your video annotation needs. If you are looking for a convenient all-in-one platform to annotate your video dataset, Isahit is the data labelling platform with the expertise and functionality to manage all your project needs.
The need for high-quality video annotation for AI training and machine learning has never been greater than it is now. Discover in this article the top 3 services to outsource your video labeling project to.
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