Adaptive morphological network based UAV target tracking algorithm

LIU Zhenbao; MA Bodi; GAO Honggang; YUAN Jinbiao; JIANG Feihong; ZHANG Junhong; ZHAO Wen

doi:10.7527/S1000-6893.2021.24904

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 4: 524904 - 524904

DOI: https://doi.org/10.7527/S1000-6893.2021.24904

Electronics and Electrical Engineering and Control

Adaptive morphological network based UAV target tracking algorithm

LIU Zhenbao ,
MA Bodi ,
GAO Honggang ,
YUAN Jinbiao ,
JIANG Feihong ,
ZHANG Junhong ,
ZHAO Wen

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1. School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710000, China;
2. Flight Control System Design Institute, AVIC the First Aircraft Design Institute, Xi'an 710089, China

Received date: 2020-10-20

Revised date: 2020-12-15

Online published: 2021-04-27

Supported by

National Natural Science Foundation of China (52072309); Key Research and Development Program of Shaanxi (2019ZDLGY14-02-01); Shenzhen Fundamental Research Program (JCYJ20190806152203506); Aeronautical Science Foundation of China (ASFC-2018ZC53026); State Scholarship Fund (201906290246)

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Abstract

To solve the problems such as the change of target direction, change of target occlusion and lack of sample diversity in the process of target tracking based on UAV images, this paper proposes a UAV aerial image target tracking algorithm based on the adaptive morphological network. First, the data-driven method is used to expand datasets, and multi rotation angle samples and occlusion samples are added to improve the diversity of samples. The proposed adaptive morphological network improves the deep belief network by rotating invariant constraints to extract deep features with strong representativeness, which enables the model to automatically adapt to the changes of target morphology. The deep feature transformation algorithm is used to obtain the pre-location area of the target to be detected. The target is located adaptively and accurately by the search agent based on the Q-learning algorithm. The category information of the tracking target is extracted by using the deep forest classifier, and the target tracking results with high precision are obtained. Comparative experiments are then carried out on several datasets. The experimental results show that the algorithm can achieve high tracking accuracy, adapt to the change of target angle and occlusion, and has good accuracy and robustness.

Key words： target tracking; UAV image; deep belief network; Q-learning algorithm; target shape change

Cite this article

LIU Zhenbao , MA Bodi , GAO Honggang , YUAN Jinbiao , JIANG Feihong , ZHANG Junhong , ZHAO Wen . Adaptive morphological network based UAV target tracking algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(4) : 524904 -524904 . DOI: 10.7527/S1000-6893.2021.24904

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