Human Activity Recognition System from Different Poses with CNN

In the principle of Human Activity Recognition, a variety of real-life implementations are available using different types of sensors such as fitness monitoring, day-life monitoring, health monitoring, etc. Especially for the elders, sensor-based applications are not feasible due to many reasons such as carrying a mobile phone or gadgets. In this paper, we focused on CCTV videos and camera images to detect human poses using HAAR Feature-based Classifier and recognize the activities of the human using the Convolutional Neural Network (CNN) Classifier. Our Human Activity Recognition System was trained using our own collected dataset which is composed of 5648 images. The approach accomplished an efficacious detection accuracy of 99.86% and recognition accuracy of 99.82% with approximately 22 frames/second after 20 epochs.