Human Activity Recognition Using Deep Learning

In this era, technology has significantly simplified people's lives, and one of the recent advancements in artificial intelligence is deep learning. Deep learning has emerged as a field that enables the creation of intelligent software and machines capable of assisting individuals in their daily tasks. One notable application of deep learning is Human Activity Recognition (HAR). Deep learning, a subset of machine learning, is used effectively to identify human activities. In this project, we used a model based on Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN) to detect human activities. This model is trained on the UCF50 dataset, which allows rigorous testing and validation. A dataset is created from the main dataset (UCF 50) with 10 action categories, and further, the dataset is split into two parts: testing and validation. Using the subsequent dataset, the ConvLSTM model accuracy is 81.4%, and the LRCN model accuracy is 85.3%.