LAHAR-CNN: human activity recognition from one image using convolutional neural network learning approach. (21st June 2021)

• Record Type: Journal Article
• Authors: Basly, Hend; Ouarda, Wael; Sayadi, Fatma Ezahra; Ouni, Bouraoui; Alimi, Adel M.
• Is Part Of: International journal of biometrics. Volume 13:Number 4(2021)
• Page Start: 385
• Page End: 408
• Publication Date: 2021-06-21
• Subjects: human activity recognition -- convolutional neural network -- CNN -- deep learning -- daily living activity; Biometric identification -- Periodicals; Biometry -- Periodicals; 570.15195
• Languages: English
• ISSNs: 1755-8301

Abstract

The problem of human action recognition has attracted the interest of several researchers due to its significant use in many applications. With the great success of deep learning methods in most areas, researchers decided to switch from traditional methods-based hand-crafted feature extractors to recent deep learning-based techniques to recognise the action. In the present research work, we propose a learning approach for human activity recognition in the elderly based on convolutional neural network (LAHAR-CNN). The CNN model is used to extract features from the dataset, then, a multilayer perceptron (MLP) classifier is used for action classification. It has been widely admitted that features learned using a CNN model on a large dataset can be successfully transferred to an action recognition task with a small training dataset. The proposed method is evaluated on the well-known MSRDailyActivity 3D dataset. It has shown impressive results that exceed the performances obtained in the state of the art using the same dataset, thus reaching 99.4%. Furthermore, our proposed approach predicts human activity (HA) from one single frame sample which justifies its robustness. Hence, the proposed model is ranked at the top of the list of space-time techniques.