TY - GEN
T1 - An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network
AU - Zheng, Yuhuan
AU - Wang, Dianwei
AU - Han, Pengfei
AU - Ren, Xincheng
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/24
Y1 - 2021/9/24
N2 - Unmanned Aerial Vehicle (UAV) has been widely used in military and civilian fields, and object tracking is one of the critical technologies in UAV application. For addressing deformation, occlusion, small object, and other UAV object tracking problems, an UAV video object tracking algorithm based on Siamese Attention Network (SANet) is proposed in this paper. Initially, we designed a lightweight network as an extractor to extract features. After that, the attention mechanism module is constructed to screen out the feature map's semantic attributes, and the corresponding weights are re-assigned to different channels and spatial features. Finally, three Regional Proposal Networks (RPNs) are introduced to hierarchical fusion to obtain the tracking results. Our proposed algorithm in this paper has experimented on the UAV123 dataset and self-built dataset. The results show that the algorithm has a good tracking effect, the average accuracy is improved to 0.815, and the success rate is 0.619.
AB - Unmanned Aerial Vehicle (UAV) has been widely used in military and civilian fields, and object tracking is one of the critical technologies in UAV application. For addressing deformation, occlusion, small object, and other UAV object tracking problems, an UAV video object tracking algorithm based on Siamese Attention Network (SANet) is proposed in this paper. Initially, we designed a lightweight network as an extractor to extract features. After that, the attention mechanism module is constructed to screen out the feature map's semantic attributes, and the corresponding weights are re-assigned to different channels and spatial features. Finally, three Regional Proposal Networks (RPNs) are introduced to hierarchical fusion to obtain the tracking results. Our proposed algorithm in this paper has experimented on the UAV123 dataset and self-built dataset. The results show that the algorithm has a good tracking effect, the average accuracy is improved to 0.815, and the success rate is 0.619.
KW - Attention
KW - Object tracking
KW - Regional proposal networks
KW - Siamese network
KW - UAV video
UR - http://www.scopus.com/inward/record.url?scp=85125869866&partnerID=8YFLogxK
U2 - 10.1145/3488933.3488934
DO - 10.1145/3488933.3488934
M3 - Conference Proceeding
AN - SCOPUS:85125869866
T3 - ACM International Conference Proceeding Series
SP - 1
EP - 8
BT - AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PB - Association for Computing Machinery
T2 - 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021
Y2 - 17 September 2021 through 19 September 2021
ER -