Video annotation is a method of labeling video footage or clips that are trained for computer vision models. During the process, the machine learns to mimic the conception of human-eye to recognize an object in the video. This method involves annotating objects frame by frame to make them perceptible for the Machine Learning model. The popularity of Video annotation applications is increasing every day due to the increment in use cases across industries such as tracking human activities, geospatial, self-driving cars, and Medical AI.
What Is Video Annotation?
Video Annotation is the process of analyzing, tagging, and labeling video data. They are used for computer vision models to recognize target objects. The model learns to identify and label the video footage accurately and uncover new target objects in this process. The more precise the video annotation will result in higher accuracy. Video is a very challenging data to work with because it has motion and image combined. However, it allows experts to access more information on the object and its surroundings. In many instances, video data has contributed more significant results for Machine learning models with less cost.
Video Annotation Process
A combination of multiple tools and approaches is required to complete the video annotation project. The video annotation process can be lengthy; for example, a one-minute video contains an estimated 60 frames per second, not to mention the complexity and added layer in a video, which need an advanced annotation tool plus human intervention to annotate at a larger scale with accuracy and efficiency.
Different Technique used for Video Annotation
1. Single Frame: In this approach, the annotator performs annotations on each image one by one by dividing the video into various images. Annotators use a copy annotation frame-to-frame capability to accomplish the task. This procedure is very time-consuming, but the process is preferable when the movement of objects in the frames under consideration is less dynamic.
2. Streaming Video: In this approach, the annotator uses specific features of the data annotation tool to analyze a stream of video frames. This method is better than Single Frame as it allows the annotator to tag objects as they move in and out of the frame, allowing models to learn more effectively.
Indika AI provides video annotation services like bounding boxes, keypoints, polygons, and semantic annotations. We use the most advanced tools and technology to annotate large volumes of video images with accuracy and efficiency at an affordable price.
Rectangular/square boxes are used for object identification, labeling, and categorization. Annotators draw boxes around the object of their interest across several frames. The box has to be close to the object as feasible for an accurate depiction of an item. In addition, the boundaries are labeled appropriately for available classes and characteristics.
The polygon method is frequently employed when bounding boxes are insufficient to correctly map an object in motion or its form/shape. As the name depicts, the annotators have to draw lines by placing dots around the outer border of the item precisely. This method is preferred when the shape of the object is unpredictable, and high accuracy is vital.
Key-point annotation is a highly used method for detecting small objects. It generates dots throughout the image and then links those dots to build a skeleton of the object of interest across each frame. The applications of keypoint annotations include detection of facial features, expressions, emotions, human body parts, sports analytics, etc.
Semantic annotation is used for each robot to recognize lanes and borders, mainly in the autonomous driving sector. In this approach, the annotators draw the object's outlines so that the model can identify across frames.
Use of Video Annotations
Like video annotations, Image Annotations are also capable of identifying and recognizing objects. So why is video annotation required? The answer is its proficiency in building the training data set for visual perception-based AI models and localizing the objects in the video for computer vision object localization. A video contains various objects, and localization helps in searching for the primary item in the image, which is the most concentrated and apparent in the frame. Thus, the primary goal of object localization is to find the object in an image and its bounds.
Another important use of video annotation is the machine's ability to follow human movements and predict postures by training the computer vision-based, AI, or machine learning models, which is highly used in sports fields to track the activities of athletes during matches and sporting events, allowing models and automated machines to learn human postures. Video annotation is also used to capture the specific item frame by frame and make it machine-readable.
Why Indika for Video Annotation services?
What matters the most in video annotation service? First, the experience and expertise as AI programs function well only with concisely labeled data. At Indika, our professionals can annotate various types of videos innovatively that lead to high-quality machine learning models.