TY - GEN
T1 - UAV Target Tracking Algorithm Based on Kernel Correlation Filter
AU - Qu, Jingkun
AU - Xu, Jinxiang
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - At present, there are still some problems in the tracking of ground targets by UAV, such as the poor tracking effect when the targets are moving rapidly, rotating and the target size is small. Therefore, this paper proposes an improved kernel correlation filtering algorithm. Firstly, the target is segmented adaptively according to the length-width ratio, and the maximum response is calculated by using the fusion feature, which is obtained by extracting the HOG feature and CN feature of each sub-block. Secondly, the position filter is used to locate the target, and the size filter estimates the size of the target. Finally, a re-detection mechanism is introduced to judge whether to update the filter based on the APCE value. The experimental results show that in the process of UAV target tracking, the improved algorithm can effectively reduce the influence of external interference on the tracking effect and improve the tracking effect.
AB - At present, there are still some problems in the tracking of ground targets by UAV, such as the poor tracking effect when the targets are moving rapidly, rotating and the target size is small. Therefore, this paper proposes an improved kernel correlation filtering algorithm. Firstly, the target is segmented adaptively according to the length-width ratio, and the maximum response is calculated by using the fusion feature, which is obtained by extracting the HOG feature and CN feature of each sub-block. Secondly, the position filter is used to locate the target, and the size filter estimates the size of the target. Finally, a re-detection mechanism is introduced to judge whether to update the filter based on the APCE value. The experimental results show that in the process of UAV target tracking, the improved algorithm can effectively reduce the influence of external interference on the tracking effect and improve the tracking effect.
KW - Feature fusion
KW - Kernel correlation filtering
KW - Target block
KW - Target tracking
UR - http://www.scopus.com/inward/record.url?scp=85130869850&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9492-9_123
DO - 10.1007/978-981-16-9492-9_123
M3 - Conference contribution
AN - SCOPUS:85130869850
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 1234
EP - 1243
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -