Algorithm Used in Gesture Recognition
Gesture recognition technologies utilize some sophisticated machine learning (ML) algorithms to interpret and classify human gestures with high accuracy. These algorithms, are trained on extensive datasets of gesture patterns, and are adaptable of recognizing and learning from the complexities of human movement. The Machine learning algorithms used in gesture recognition include:
- Convolutional Neural Networks (CNNs): Convolutional Neural Networks excel in processing visual data and identifying spatial hierarchies in gestures.
- Support Vector Machines (SVMs): Support Vector Machines is used for their robustness in classifying gestures into distinct categories based on extracted features.
- Hidden Markov Models (HMMs): Hidden Markov Models is ideal for analyzing temporal dynamics and sequences in gesture movements.
- Decision Trees: Decision Trees facilitate straightforward decision-making processes based on gesture characteristics.
What is Gesture Recognition? Use Cases, Technologies and Algorithms
Gesture recognition is a touchless technology that allows devices to understand and respond to human movements as commands. Gesture recognition technology is integrated into a variety of appliances and devices, most commonly known are – the Microsoft Kinect for Xbox and Play Station games have used gesture recognition. In this article, we are going to discuss gesture recognition.