In recent year, due to motility and wide coverage, unmanned aerial vehicle (UAV) has been widely applied in surveillance system. Human action recognition in UAV video is essential for surveillance video understanding. However, existing action recognition methods suffer from heavy computing, which makes it hard to deploy in real applications. In this paper, a lightweight action recognition method for UAV video(LARMUV) is proposed. This method is based on TSN and adopt Mo-bileNetV3 as backbone, which greatly reduces amount of computing and parameters. Self-attention mechanism is adopted to capture temporal structure among different frames. For loss function, Focal Loss is used to putting more focus on hard, misclassified examples. Last but not least, knowledge distillation is employed to enhance the performance of our model, which transfer knowledge from a larger teacher model to student model. Experimental results on HMDB51, UCF101 and UAV dataset show that our method can achieve competitive performance compared to baseline methods while run in real-time mode.
|Title of host publication||2020 IEEE 3rd International Conference on Electronics and Communication Engineering, ICECE 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 17 Feb 2021|
|Event||3rd IEEE International Conference on Electronics and Communication Engineering, ICECE 2020 - Virtual, Xi'an, China|
Duration: 14 Dec 2020 → 16 Dec 2020
|Name||2020 IEEE 3rd International Conference on Electronics and Communication Engineering, ICECE 2020|
|Conference||3rd IEEE International Conference on Electronics and Communication Engineering, ICECE 2020|
|Period||14/12/2020 → 16/12/2020|
Bibliographical noteFunding Information:
This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (ASFC-20175152036) and Key Project on Artificial intelligence(1004-56XZA19008). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education.
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- action recognition
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Electrical and Electronic Engineering