Yadav, S. K., Luthra, A., Pahwa, E., Tiwari, K., Rathore, H., Pandey, H. and Corcoran, P., 2023. DroneAttention: Sparse weighted temporal attention for drone-camera based activity recognition. Neural Networks, 159, 57-69.
Full text available as:
|
PDF
Drone_One_Elsevier.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. 17MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Official URL: https://www.sciencedirect.com/journal/neural-netwo...
DOI: 10.1016/j.neunet.2022.12.005
Abstract
Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.
Item Type: | Article |
---|---|
ISSN: | 0893-6080 |
Uncontrolled Keywords: | Action Recognition; Sparse Weighted Temporal Attention; Drone Vision |
Group: | Faculty of Science & Technology |
ID Code: | 37900 |
Deposited By: | Symplectic RT2 |
Deposited On: | 15 Dec 2022 16:38 |
Last Modified: | 13 Dec 2023 01:08 |
Downloads
Downloads per month over past year
Repository Staff Only - |