Human Regular Activities Recognition Using Convolutional Neural Network

Abstract

Capturing commonly occurring behaviors is a tough issue in computer vision. A few of them are recreation, touring, leisure pursuits, and religious practice. A comprehensive effort has already been dedicated to this aspect to deal with this issue. In this work, we recreated a dataset with five categories, including household activities, farming, exercise, sports, and occupation, to identify human daily actions. This collection has 4328 colored images in total, among them 630 are set aside for testing, and 3698 for training. Deep learning and standard image-based strategies are being explored to address the issues. In this paper, we have designed a deep learning paradigm to classify the regular activities of human beings. To characterize people's daily chores, we use the CNN model, one of the greatest tools for visual identification. We also have chosen two already-trained VGG16 and ResNet50 models. When we compare our model with the existing techniques, the investigation's findings demonstrate that the suggested network has a better recognition accuracy of 91%. Additionally, we have observed that accuracy varies throughout different epochs, and after 25 epochs we got better stable results from our model. The reader may find this article instructive in grasping CNN models for various recognizing applications.