Author:
Low Kah Sin,Eng Swee Kheng
Abstract
Abstract
Human Activity Recognition (HAR) is crucial in various applications, such as sports and surveillance. This paper focuses on the performance evaluation of a HAR system using deep learning techniques. Features will be extracted using 3DCNN, and classification will be performed using LSTM. Meanwhile, 3DCNN and RNN are two additional, well-known classification techniques that will be applied in order to compare the effectiveness of the three classifiers. The 3DCNN-LSTM approach contributes the highest overall accuracy of 86.57%, followed by 3DCNN-3DCNN and 3DCNN-RNN with the overall accuracy of 86.07% and 79.60%, respectively. Overall, this paper contributes to the field of HAR and provides valuable insights for the development of activity recognition systems.
Subject
Computer Science Applications,History,Education
Reference14 articles.
1. Two-stream convolutional networks for action recognition in videos;Simonyan,2014
2. Video-Based Human Activity Recognition Using Deep Learning Approaches;Surek;Sensors,2023
3. 3D convolutional neural networks for human action recognition;Ji;IEEE transactions on pattern analysis and machine intelligence,2012