Key Point annotation is a more detailed protocol of image annotation used to detect small objects and shape variations by marking locations of Key Points. Key Point annotations are used to label a single pixel in the image to portray an object's shape. It is a very precise technique that has its uses in movement tracking and prediction, human body parts detection, emotion, gesture and facial recognition. It is commonly used in sports and security.
Image Annotation Techniques
Artificial intelligence requires human intervention . The goal of image annotation is to assign relevant, task-specific labels to images so that it is easily understood by the AI. The Image segmentation process consists of the following techniques:
- Image classification: Identify the contents of the image such as person, car, tree, etc.
- Object Detection: It is a method to identify and correctly label every object present in an image frame.
This broadly consists of two steps :
- Object Localization: Locating the exact position of the object by determining the enclosing region/bounding box in the tightest manner.
- Image Classification: Labelling the object.
- Semantic Segmentation: It involves detecting objects within an image and putting them in groups of defined categories such as Humans, Vehicles, Traffic lights, etc.
- Instance Segmentation: It can be considered a refined version of Semantic Segmentation. It involves identifying each object instance for every known object within an image. That means it treats multiple objects of the same group as separate objects.
- Panoptic Segmentation: It is a combination of Instance and Semantic Segmentation. Each pixel is associated with two values- its group label and an instance number. It can also recognize the background elements like sky, road, grass, etc.
- Key Point Annotation / Landmark Recognition: It involves setting up a skeletal structure of the image. Comparing the nodes and edges that compose the 'skeleton' of an object is accomplished through Key Point annotation. This can then be used to track the shapes of specific objects by allowing the neural network to recognize essential points of interest in the input image. The neural network outputs the coordinates (x, y) of key points. By tracking multiple Key Points, facial features and emotions can easily be recognised. It can also be used to identify tactical weaknesses by combining it with other annotation techniques.
Key Point Annotation in Sports
One of the major applications of Key Point annotation is in the field of sports. Modern athletes require the latest technology to complement their physical training. Incorporating image annotation for analyzing performances is an example of that. Key Point annotation can be used to track and recognize miniscule performance improvements that may go unmissed by the human eye. It can also provide an early warning system for various types of injuries. Image annotation helps athletes to compete at the highest level and maintain consistent performance there.
Challenges with Key Point annotation
- Time: Manually annotating key points in an image is a time consuming task. Machine learning requires a huge amount of training data. It requires a lot of time and patience to accurately effectively label the image-based datasets.
- Computational Complexity: Any kind of error while labeling key points can affect the training process and ruin the whole work.
- Domain Knowledge: High domain knowledge in a given field is often required to assign accurate labels. Annotators should know what to label and have some level of expertise in the particular field.
Why Annotation Support?
We will design a workflow that ensures strict adherence to quality guidelines provided by our customers. We have meticulously assembled a specialist team of annotators for any type project using a stringent testing process. To deliver our quality data sets, we employ a combination of custom automated checks and expert human evaluation to rectify any errors in the annotated data.Particular attention is paid to Key Point visibility and consistency in both individual frames and the dataset as a whole. This produces annotations that track movement to a high degree of accuracy.