Vision based Fall Detection using Optimized Convolutional Neural Network

Health monitoring and tracking using Human Activity Recognition is a burgeoning study area, especially when it comes to detecting falls in the elderly. Falls can result in injuries, bodily injury, fractures, and other problems. Visual surveillance's major goal is to replace traditional video surveillance with intelligent visual surveillance. Different feature extraction algorithms were developed to better detect the fall. Convolutional Neural Network (CNN) in Fall Detection with the Optical Flow algorithm is used to extract features to find the fall detection. Designing CNN architecture for classification purposes without overfitting is a challenging task. The goal of this work is to avoid overfitting in training by automatically optimizing the CNN architecture with fine tuned dropout rate. This helps to increase the efficiency of the model. Four different CNN architecture are developed to predict fall detection. The UP-FALL Detection Dataset is used to test the performance of the four models. The experimental results show that second model produced high accuracy compared with other models.