Aiming at the problem that targets are subject to occlusion, deformation, and complex background interference in the drone video, a Unmanned Aerial Vehicle (UAV) target tracking algorithm based on the adaptive depth network is proposed. First, based on the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN), a 3-order adaptive CNN network is designed for target feature extraction. PCA is hierarchically performed on H,S, and I channels, convolving hierarchically by the obtained eigenvectors, which optimizes the network structure and improves the convergence speed and accuracy. Second, the target depth feature is input into KCF algorithm for target tracking. By analyzing the change rate of the two adjacent frames and using the segmented adaptive adjustment of learning rate to update the target template, the target occlusion problem is effectively moderated. The experimental results show that the algorithm effectively avoids the degradation of tracking accuracy caused by complex factors, reaching good robustness. The average accuracy-rate of the algorithm is 9.62% higher than that of fully convolutional network based tracker Fully Convolutional Network Tracking (FCNT), and the average success-rate is increased by 11.9%.
LIU Fang
,
WANG Hongjuan
,
HUANG Guangwei
,
LU Lixia
,
WANG Xin
. UAV target tracking algorithm based on adaptive depth network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019
, 40(3)
: 322332
-322332
.
DOI: 10.7527/S1000-6893.2018.22332
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