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SWTA: Sparse weighted temporal attention for drone-based activity recognition.

Yadav, S. K., Pahwa, E., Luthra, A., Tiwari, K. and Pandey, H. M., 2023. SWTA: Sparse weighted temporal attention for drone-based activity recognition. In: 2023 International Joint Conference on Neural Networks (IJCNN). New York: IEEE, 1-8.

Full text available as:

Drone_IJCNN (2).pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


Official URL:

DOI: 10.1109/IJCNN54540.2023.10191750


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.

Item Type:Book Section
Additional Information:Gold Coast, Australia, 18-23 June 2023.
Uncontrolled Keywords:Human Activity Recognition; Video Understanding; Drone Action Recognition
Group:Faculty of Science & Technology
ID Code:38505
Deposited By: Symplectic RT2
Deposited On:17 May 2023 11:27
Last Modified:24 May 2024 09:22


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