Keypoint Detection: What, When and How
Markets and Markets report highlights, “The AI in computer vision market was valued at USD 2.37 billion in 2017 and is expected to reach USD 25.32 billion by 2023, at a CAGR of 47.54% during the forecast period”. The key component of this growth can be attributed to the realization of the impeccable applications of computer vision in ways unimaginable. Did you use your smartphone today to take a selfie, Well computer vision was working under the hood to finalize that perfect portrait mode for you. When talking of a smartphone, how did you unlock it? Did you use your finger to activate the touch sensor? Well, computer vision does its trick here too. Or did you use the newest face unlock feature? Yes, you guessed it right the wizard here is computer vision.
Let us uncover the key terminology behind such marvellous applications. Landmark/ keypoint/ dot detection as you know it is a computer vision technique that aims to recognise essential features in an input image. One of its primary use is detecting and quantifying small objects. Take for example the eyes of a person.
The detection and quantification of objects and features can then be attributed to the detection of specific landmarks like cars, trees etc. in the case of aerial photography or the detection of emotions from the landmark features of the face or pose estimation by detecting the landmarks of the body.
Some of the AI use cases where dot annotation can be used are:
Face recognition systems employ computer algorithms to identify specific, distinguishing features on a person’s face. These details, such as the distance between the eyes or the shape of the chin, are then mathematically represented and compared to data on other faces collected in a face recognition database. The data about a specific face is often referred to as a face template, and it is distinct from a photograph in that it is intended to include only specific details that can be used to distinguish one face from another.
The face template for training the face recognition model is a facial image with keypoint facial features annotated as dots on the image. The dots then account for specific features and feature details important for facial template mapping in the database
Fashion landmark detection
Computer vision AI-driven fashion landmark technology is one of the ways that aims to predict the positions of functional key points defined on clothing, such as the corners of the neckline, the hemline, and the cuff.
Not only do these landmarks indicate the functional regions of clothing, but they also implicitly capture their bounding boxes, forming the design, pattern, and category of clothing.
Scale variations and non-rigid deformations in clothing lead to increased challenges in more complex patterns than restricted deformations, makes landmark detection even more data-hungry to satisfy algorithms and this differentiates it from known pose estimation.
Dot annotation of the functional keypoint positions defined on the clothing is helpful in annotating the images to be fed to the fashion landmark detection model.
The method of identifying human emotions from facial expressions is known as facial emotion detection. The human brain recognizes emotions instinctively, the task at hand is to automate this process and make machines do the same with ease. Artificial Intelligence and computer vision can be helpful for this purpose. This research is improving all the time and will soon be able to read feelings as well as our minds.
AI can detect emotions by studying the meaning of each facial expression and applying the experience to new information introduced to it. Emotional artificial intelligence (AI) is a technology that can read, imitate, translate, and react to human facial expressions and emotions. For the study of facial expressions, facial emotion templates need to be created. Facial landmark annotation helps in drafting the emotion templates as training data for the Deep learning models to be trained. An ensemble of face detection and facial landmark detection models can be employed along with an emotion template classification model to aptly detect the emotion in a facial image.
Pose estimation is a computer vision task that determines the pose of a person or object in a picture or video. Pose estimation may also be described as the problem of determining the location and orientation of a camera in relation to a given individual or object.
Usually, this is accomplished by finding, locating, and monitoring a series of keypoints on a given item or individual. These may be corners or other distinguishing characteristics of an entity. In humans, these keypoints correspond to major joints such as the elbow or a knee. The deep learning-based computer vision’s aim is to monitor these keypoints in images and videos.When considering people, keypoints reflect big joints such as elbows, ankles, hands, and so on. This is known as human pose prediction.
The use cases are marvellous but one bottleneck remains. The high quality labelled dataset to train your deep learning models to adhere to any one of the above-mentioned use-cases.
Are you encountering a similar problem? Training a Computer vision model requires a voluminous amount of high quality labelled data. Which in turn needs a sophisticated enterprise-scale, cloud-based annotation system. Where you can plug your data stream from the cloud/ on-prem based databases and get labelled data with confidence scores for training the computer vision models and accounting for a cent per cent data privacy.
If yes. I got your back. Let me introduce you to a one-stop solution to all your machine learning needs. Labellerr
Labellerr is a data-annotation platform that provides simple, clear and easy to use UI with seamless UX to perform annotation on different types of data catering to a wide array of industries, Retail, Health care, E-commerce, Hospitality, Businesses to name a few.
For your, AI needs Labellerr provides pre-trained models and products that can deliver Plug n-Play APIs with basic functionality to reach clients and can be modified on request easily and swiftly.
Leverage the auto-label feature on Labellerr to annotate your data with 10x speed and save crucial man-hours.
Follow along with this video to get an idea of how to perform dot annotation on Labellerr.
Automated ML experience on Labellerr
Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob etc.
Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.
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