Human Body Poses Recognition Using Neural Networks with Class Based Data Augmentation
Name
Karl-Kristjan Luberg
Abstract
Information technologies and continuous development of information technologies have made it possible for computers to see and learn. What we see with our eyes, can be divided into pixels and fed to a computer, giving the computer the ability to see and learn based on the pixel values. Based on the input values computers can learn to recognize different objects depending on the examples taught to them. There are many possible applications for computers to see and learn in order to solve new tasks. In this thesis, we propose a framework, capable of automatically recognizing human body poses from a single image, obtained with a traditional low-cost camera. Our approach combines computer vision with neural networks to detect a human from an image. This process starts by extracting the silhouette from an image and then using a neural network to recognize body poses based on the extracted silhouettes. In order to match detected silhouettes with body poses, the neural network was trained with an already classified augmented dataset of preprocessed images depicting silhouettes. According to our results, we show that the proposed method provides promising results with acceptable accuracy.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Amnir Hadachi, Artjom Lind
Defence year
2018