Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model

摘要

Currently, image acquisition and understanding has become a necessity. In fact, it’s what allows machines to become one of the most powerful tools.Nowadays, machines that replace humans and experts in making decisions in several areas have seen their success from what so-called deep learning, a powerful learning in Machine learning tool for processing, classifying and object recognition tasks. The idea behind deep learning is training machines, adapting their skills and applying them to many tasks. In the same way that human brain learns, the information is entered through our senses (eyes...) and goes through billions of neurons before it was processed to get an output, deep learning also takes information as in input then start proceeding through several hidden layers before an output layer.For that, we choose to profit from this powerful learning to improve the Video fall detection algorithm which suffers from generating a huge amount of false alarms. So we propose in our work to minimize these false alerts using a CNN model that can classify a person sitting in a wheelchair from others to eliminate them.We present in this paper, on the one hand, a survey of the most recent and powerful architectures of CNN, on the other, we propose to add a CNN model into the elderly person fall Video-Detection Algorithm to improve its accuracy.