SWTA: Sparse Weighted Temporal Attention for Drone-Based Activity Recognition
Authors: Kumar Yadav, S., Pahwa, E., Luthra, A., Tiwari, K. and Pandey, H.M.
Journal: Proceedings of the International Joint Conference on Neural Networks
Volume: 2023-June
ISBN: 9781665488679
DOI: 10.1109/IJCNN54540.2023.10191750
Abstract:Drone-camera based human activity recognition (HAR) has received significant attention from the computer vision research community in the past few 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 divided into two components. 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.
https://eprints.bournemouth.ac.uk/38505/
Source: Scopus
SWTA: Sparse Weighted Temporal Attention for Drone-Based Activity Recognition
Authors: Yadav, S.K., Pahwa, E., Luthra, A., Tiwari, K. and Pandey, H.M.
Journal: 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN
ISSN: 2161-4393
DOI: 10.1109/IJCNN54540.2023.10191750
https://eprints.bournemouth.ac.uk/38505/
Source: Web of Science (Lite)
SWTA: Sparse Weighted Temporal Attention for Drone-Based Activity Recognition
Authors: Pandey, H., Yadav, S., Pahwa, E., Luthra, A. and Tiwari, K.
Conference: International Joint Conference on Neural Networks (IJCNN 2023)
Dates: 18-23 June 2023
Abstract:Drone-camera based human activity recognition (HAR) has received significant attention from the computer vision research community in the past few 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 divided into two components. 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.
https://eprints.bournemouth.ac.uk/38505/
https://ieeexplore.ieee.org/Xplore/home.jsp
Source: Manual
SWTA: Sparse weighted temporal attention for drone-based activity recognition
Authors: Yadav, S.K., Pahwa, E., Luthra, A., Tiwari, K. and Pandey, H.
Conference: International Joint Conference on Neural Networks (IJCNN 2023)
Publisher: IEEE
Abstract:Drone-camera based human activity recognition (HAR) has received significant attention from the computer vision research community in the past few 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 divided into two components. 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.
https://eprints.bournemouth.ac.uk/38505/
Source: BURO EPrints