Αναγνώριση χειρονομιών σε βίντεο πραγματικού χρόνου με αλγορίθμους μηχανικής μάθησης και βαθιάς μάθησης
Computer Vision and Machine Learning are two branches of Artificial Intelligence which in combination have granted computational systems the ability of artificial vision and perception. Nowadays, human-machine interaction and the analysis of human behavior can be achieved using cameras. The subject of the current thesis is the implementation of a system for automatic recognition of static hand gestures in real-time videos with cameras, using modern techniques and algorithms of Machine Learning and Deep Learning. To begin with, we make an introduction into Computer Vision and its applications and we describe the problem of hand gesture recognition. Next, we refer to the field of Machine Learning and the main categories of the problems it includes. We describe the methodology of training a model and ways to measure its performance, focusing on Classification problems. Also, we introduce Deep Learning and Artificial Neural Networks. We present the most common network architectures used nowadays and the procedure of training a Neural Network and methods for optimization. Also, we mention the use of Convolutional Neural Networks for solving Computer Vision problems and we present some of the most known algorithms used for this task. Finally, we present our implementation of the hand gesture recognition tool, using Python programming language. We apply innovative algorithms of Convolutional Neural Networks for the detection of several key points of the hand and we train a Support Vector Machine and a Feedforward Neural Network model for the classification of those points into several predetermined static gestures. The implementation is targeted for practical real-time applications, runs using the CPU of the computer and requires a common RGB camera.