Mhad: Multi-human action dataset
| Author | Elharrouss O. |
| Author | Almaadeed N. |
| Author | Al-Maadeed, Somaya |
| Available date | 2022-05-19T10:23:11Z |
| Publication Date | 2020 |
| Publication Name | Advances in Intelligent Systems and Computing |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1007/978-981-15-0637-6_28 |
| Abstract | This paper presents a framework for a multi-action recognition method. In this framework, we introduce a new approach to detect and recognize the action of several persons within one scene. Also, considering the scarcity of related data, we provide a new data set involving many persons performing different actions in the same video. Our multi-action recognition method is based on a three-dimensional convolution neural network, and it involves a preprocessing phase to prepare the data to be recognized using the 3DCNN model. The new representation of data consists in extracting each person?s sequence during its presence in the scene. Then, we analyze each sequence to detect the actions in it. The experimental results proved to be accurate, efficient, and robust in real-time multi-human action recognition. |
| Sponsor | This publication was made by NPRP Grant# NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
| Language | en |
| Publisher | Springer |
| Subject | Convolution Neural networks Action recognition Convolution neural network Convolutional neural network Human actions Human-action recognition New approaches Preprocessing phase Video surveillance Security systems |
| Type | Conference |
| Pagination | 333-341 |
| Volume Number | 1041 |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||
This item appears in the following Collection(s)
-
Computer Science & Engineering [2428 items ]