Video Annotation: A Complete Guide

The process of adding annotations to videos is known as video annotation or video labelling. The primary goal of video annotation is to make it easier for computers to identify objects in videos using AI-powered algorithms. Annotated videos create a high-quality reference database that computer vision-enabled systems can use to accurately identify objects like cars, people, and animals. With an increasing number of everyday tasks relying on computer vision, the value of video annotation cannot be overstated.

Video Annotation is one of the annotation processes that requires labeling target objects in video footage. This information is generally added to videos by human annotators who apply outlines and labels to video frames in line with the specific requirements of each machine learning model.In most cases, video annotation means teams of annotators locating relevant objects in each frame of video data.

Most commonly, annotators use bounding boxes to pinpoint objects that machine learning engineers have designated as important to label. These boxes will then be assigned a colour and a label. Different machine learning projects require different ranges of objects to be labeled, in different ways.

Video Annotation for Machine Learning:

While video annotation is useful for detecting and recognizing objects, its primary purpose is to create training data sets. When it comes to video annotation, there are several different steps that apply.

• Frame-by-frame detection – With frame-by-frame detection, individual items of interest are highlighted and categorized. By capturing specific objects, detection with ML algorithms can be improved.
• Object localization – object localization helps to identify specific images within a defined boundary. This helps algorithms find and locate the primary object in an image.
• Object tracking – often used with autonomous vehicles, object tracking helps detect street lights, signage, pedestrians, and more to improve road safety.
• Individual tracking – similar to object tracking, individual tracking is focused on humans and how they move. Video annotation at sporting facilities helps ML algorithms understand human movement in different situations.

Various Methods for Video Annotation:

Bounding Boxes

Bounding boxes are a video annotation technique in which annotators draw a box around a specific object or image in a video. The box is then annotated so that computer vision tools can automatically identify similar objects in videos. This is one of the most common methods of video annotation.

3D Cuboids

Cuboids are useful for marking up objects in three dimensions. We can describe the size, orientation, and location of an object in a frame using this form of annotation. It is especially helpful for annotating 3D-structured things like furniture and cars.

Polygon Annotation

Unlike bounding box annotation, polygon annotation can be used to identify more complex objects. Any object, regardless of shape, can be annotated with a polygon annotation. This type of video annotation is ideal for objects with complex shapes, such as people and vehicles.

Semantic Segmentation

Images in videos are labelled using a variety of image annotation techniques. It has the ability to label certain parts of an image up to full segmentation. The semantic meaning of every pixel is tagged, enabling the computer vision model to operate at the highest level of accuracy.

Key Point Annotation

Keypoints are quite helpful for video annotations if we don’t need to worry about the shape of an object. Key point annotation is commonly used to identify small objects, shapes, postures, and movements.

Video Annotation Techniques:

Single frame annotation:

The traditional method of single image video annotation extracts each frame from the video and annotates each frame individually. The video is divided into frames, and each image is annotated in the traditional way. The target object’s annotated in every frame of the video. In complex scenarios, single frame annotation is always used because it ensures quality.

Streamed frame annotation:

The continuous frame method of video annotation can be streamlined with automation technologies. Frame-by-frame tracking of objects and their locations can be done automatically by computers, maintaining the information’s continuity and flow. In order to assess the pixels in the previous and subsequent frames and forecast the motion of the pixels in the current frame, computers rely on continuous frame techniques like optical flow. With this amount of information, the computer can correctly identify an object that is visible at the start of the video, then vanishes for a number of frames before reappearing later. Teams can mistakenly identify that thing as a different object when it reappears if they instead utilise the single image method. This approach nevertheless has its share of difficulties. Low-resolution captured video, such as that used for surveillance, is possible. Engineers are working to develop interpolation technologies like optical flow to better utilise context across frames for object recognition in order to address this issue.

Benefits of Video Annotation:

1. You can use interpolation. You don’t have to annotate every single frame with AI annotation tools. You can sometimes annotate the start and finish of your series and then interpolate between them. The annotations in between will be generated automatically.
2. The temporal context opens up new opportunities. Videos involve motion, which can be challenging to train for a static image-based AI model. By annotating videos, you can help the AI model learn how objects move and change over time.
3. Improved data for training AI models. Images are not as detailed as videos. Annotating a video gives the AI system more data to work with, which can lead to more accurate results.
4. It is cost efficient. A single video has more data points than a single image. And by concentrating on only a few keyframes, the entire process is less time consuming.

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