基于自适应深度网络的无人机目标跟踪算法

doi:10.7527/S1000-6893.2018.22332

Abstract

Abstract: 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%.

Key words: convolution neural network, principal component analysis, feature learning, correlation filter, target tracking

CLC Number:

V249
TP391

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.

References

[1] LI X, HU W, SHEN C, et al. A survey of appearance models in visual object tracking[J]. Acm Transactions on Intelligent Systems & Technology, 2013, 4(4):16-58.
[2] 高琳, 王俊峰, 范勇, 等. 基于卷积神经网络与一致性预测器的鲁棒视觉跟踪[J]. 激光与光电子学进展, 2017, 37(8):0815003-0815017. GAO L, WANG J F, FAN Y, et al. Robust visual tracking based on convolutional neural network and consistency predictor[J]. Laser & Optoelectronics Progress, 2017, 37(8):0815003-0815017(in Chinese).
[3] HARE S, GOLODETZ S, SAFFARI A, et al. Struck:Structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10):2096-2109.
[4] WANG X F, DONG X M, KONG X W, et al. Drogue detection for autonomous aerial refueling based on convolutional neural networks[J]. Chinese Journal of Aeronautics, 2017, 30(1):380-390.
[5] WANG N, YEUNG D Y. International conference on neural information processing systems[J]. Curran Associates Inc, 2013, 34(5):809-817.
[6] HONG S, YOU T, KWAK S, et al. Online tracking by learning discriminative saliency map with convolutional neural network[C]//International Conference on Machine Learning. New York:ACM, 2015:597-606.
[7] NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ:IEEE Press, 2016:4293-4302.
[8] WANG L, OUYANG W, WANG X, et al. Visual tracking with fully convolutional networks[C]//IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE Press, 2016:3119-3127.
[9] 赵洲, 黄攀峰, 陈路. 一种融合卡尔曼滤波的改进时空上下文跟踪算法[J]. 航空学报, 2017, 38(2):269-279. ZHAO Z, HUANG P F, CHEN L. Improved spatial-temporal context tracking algorithm based on fusion Kalman filter[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2):269-279(in Chinese).
[10] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE Press, 2010.
[11] HENRIQUES J F, RUI C, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//European Conference on Computer Vision. Berlin, Heidelberg:Springer, 2012:702-715.
[12] HENRIQUES J F, RUI C, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 37(3):583-596.
[13] 崔乃刚, 张龙, 王小刚, 等. 自适应高阶容积卡尔曼滤波在目标跟踪中的应用[J]. 航空学报, 2015, 36(12):3885-3895. CUI N G, ZHANG L, WANG X G, et al. Application of adaptive high-capacity Kalman filter in target tracking[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(12):3885-3895(in Chinese).
[14] CHAN T H, JIA K, GAO S, et al. PCANet:A simple deep learning baseline for image classification[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2015, 24(12):5017-5032.
[15] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9):1904-1916.
[16] HUANG S, YANG D, GE Y, et al. Combined supervised information with PCA via discriminative component selection[J]. Information Processing Letters, 2015, 115(11):812-816.
[17] MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking[J]. Far East Journal of Mathematical Sciences, 2016, 2(2):445-461.
[18] BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple:Complementary learners for teal-time tracking[C]//Computer Vision & Pattern Recognition, 2016.
[19] DANELLJAN M, HAGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[J]. Proceedings of the IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE Press, 2016:4310-4318.

UAV target tracking algorithm based on adaptive depth network

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Shaobo YAO, Lijian JIANG, Wenwen ZHAO, Zheng LU, Changju WU, Weifang CHEN. Numerical method of data-driven rarefied nonlinear constitutive relations coupled with clustering [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S2): 40-53.
[2]	CHEN Bo, YUE Kai, WANG Rusheng, HU Mingnan. Learning-based multi-rate multi-sensor fusion localization method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726904-726904.
[3]	CHEN Linxiu, YANG Xiangyu, ZHANG Hang, ZHAO Jiajia, HAO Mingrui. Composite guidance technology based on active radar/infrared information fusion [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 727058-727058.
[4]	SHAO Yiwei, CHEN Jiayu, LIN Cuiying, WAN Cheng, GE Hongjuan, SHI Zhilong. Gearbox fault diagnosis with small training samples: An improved deep forest based method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 625429-625429.
[5]	LIU Fang, SUN Yanan. UAV target tracking algorithm based on adaptive fusion network [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(7): 325522-325522.
[6]	XIONG Wei, ZHU Hongfeng, CUI Yaqi. Recurrent adaptive maneuvering target tracking algorithm based on online learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 325250-325250.
[7]	JIN Biao, KUANG Xiaofei, PENG Yu, ZHANG Zhenkai. Distributed power allocation method for netted radar based on cooperative game theory [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(1): 324776-324776.
[8]	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.
[9]	JIANG Bo, QU Ruokun, LI Yandong, LI Chenglong. Object detection in UAV imagery based on deep learning: Review [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524519-524519.
[10]	YAN Wenxu, LAN Hua, WANG Zengfu, JIN Shuling, PAN Quan. Nonlinear filtering for spaceborne radars based on variational Bayes [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S2): 724395-724395.
[11]	HUANG Jingshuai, LI Yongyuan, TANG Guojian, BAO Weimin. Adaptive tracking method for hypersonic glide target [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323786-323786.
[12]	LIANG Dong, GAO Sai, SUN Han, LIU Ningzhong. UAV detection in motion cameras combining kernelized correlation filters and deep learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323733-323733.
[13]	XI Zhifei, XU An, KOU Yingxin, LI Zhanwu, YANG Aiwu. Target threat assessment in air combat based on PCA-MPSO-ELM algorithm [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323895-323895.
[14]	YI Xiao, DU Jinpeng. Asynchronous track-to-track association algorithm based on discrete degree of segmented sequence [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(7): 323694-323694.
[15]	TIAN Chen, PEI Yang, HOU Peng, ZHAO Qian. Decision uncertainty based sensor management for multi-target tracking [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(10): 323781-323781.