Human Body Poses Recognition Using Neural Networks with Data Augmentation

The continuous development of technology of IT enabled computers to see and learn. There are many viable applications for computer learning and vision to solve new tasks. In this paper, we propose a framework, able of automatically perceiving the human body poses from a single image, acquired by a traditional low-cost camera. Our methodology exploiting the vision of computers features and neural networks to detect and recognize a human from an image. The processing start with detection human in image and then extracting the silhouette from an image then using a neural network to recognize body poses based on silhouettes that extracted. To classified the detected silhouettes with body poses, the neural network was trained with dataset of preprocessed images contains silhouettes and labelled using 0, 1 for standing and walking poses respectively labelled , according to our outcomes that resulted in an accuracy over 93% with the best neural network model. The proposed approach offers promising effects with accepted accuracy. This knowledge will help greatly in decision-making and provide independent vehicles with valuable information for their artificial intelligence to process and make decisions as regards the movement of pedestrians.