Multiscale Spatio-Temporal Network for Aerial Video Event Recognition
Feng Yang, Jian Zhang, Yue Zhao, Anyong Qin, Chenqiang Gao · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Unmanned aerial vehicles (UAVs) are widely used in the field of remote sensing because of their advantages of providing real-time and high-resolution videos at a low cost. Compared with generic video understanding, aerial video event recognition is faced with emerging challenges: 1) aerial videos contain richer scene information; 2) the scale variations between different videos are large. To address these issues, we propose a Multiscale Spatio-Temporal Network (MSTN) in this paper. More precisely, the MSTN consists of a Pyramid Spatio-Temporal (PST) module and a Multi-Time Scale Decision (MTSD) module, which learn multi-scale spatio-temporal features together. The two modules can better learn spatio-temporal characteristics and boost the performance by 3.7% compared with the baseline method. In ERA, an aerial event recognition dataset, our method achieves the state-of-the-art results.