Artificial Intelligence in Recognition of Dynamic Gestures

Abstract

The development of effective methods for recognizing human hand gestures is an urgent task, both from a scientific and an applied point of view. Gesture recognition methods are the basis of contactless interfaces for managing technical systems. The most significant areas of application of gesture recognition systems are automatic sign language translation and contactless control of technical systems. Almost all existing implementations of recognition methods work confidently when the hand is on a uniform background. But in reality, such a case of using such implementations is unlikely. The likelihood of correct recognition drops sharply against a complex background when the hand in the image crosses the face or body parts with exposed skin. Additional restrictions on algorithms impose requirements for their hardware implementation. These should be compact devices on a chip (SoC) with low consumption, size and price, and, therefore, with low computing power. Taking into account the above, it should be recognized that it is relevant to solve the problem of increasing the efficiency of algorithms and methods of gesture recognition. Two approaches to solving the problem of recognizing dynamic hand gestures are considered - analytical and neural network. It is shown that the use of artificial intelligence techniques can increase the reliability of gesture recognition in complex video surveillance conditions. However, the use of neural network algorithms cannot show high performance on single-board computers if they do not have an NPU or a powerful GPU module.

Keywords

• artificial intelligence
• analytical algorithm
• static and dynamic gestures
• contactless control of technical systems
• complex background
• recognition