In this paper, based on muti-domain network (MDNet), fast deep learning for aircraft tracking (FDLAT) algorithm is proposed to track aircraft target. This algorithm uses feature-based transfer learning to make up the inferiority of small sample sets, uses specific data sets to update parameters of convolutional layers and fully connected layers, and use it to distinguish aircraft from background. After building the training model, we put the aircraft video sets into the model and tracked the aircraft using regression model and a simple line on-line update, to increase the speed while ensuring the accuracy. This algorithm achieves robust tracking for aircraft in rotation, similar targets, fuzzy targets, complex environment, scale transformation, target occlusion, morphological transformation and other complex states, and runs at a speed of 20.36 frames with the overlap reached 0.592 in the ILSVRC2015 detection sets of aircraft, basically meets the real-time application requirement of aircraft tracking.
Application of aircraft target tracking based on deep learning
First published at:Sep 30, 2019
1 Sivanantham S, Paul N N, Iyer R S. Object tracking algorithm implementation for security applications[J]. Far East Journal of Electronics and Communications, 2016, 16(1): 1–13. DOI:10.17654/EC016010001
2 Kwak S, Cho M, Laptev I, et al. Unsupervised object discovery and tracking in video collections[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 3173–3181.
3 Luo H B, Xu L Y, Hui B, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 502002.
4 Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–577. DOI:10.1109/TPAMI.2003.1195991
5 Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1822–1829.
6 Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596. DOI:10.1109/TPAMI.2014.2345390
7 Fan X S, Xu Z Y, Zhang J L. Dim small target tracking based on improved particle filter[J]. Opto-Electronic Engineering, 2018, 45(8): 170569. DOI:10.12086/oee.2018.170569
樊香所, 徐智勇, 张建林.改进粒子滤波的弱小目标跟踪[J].光电工程, 2018, 45(8): 170569. DOI:10.12086/oee.2018.170569
8 Xi Y D, Yu Y, Ding Y Y, et al. An optoelectronic system for fast search of low slow small target in the air[J]. Opto-Electronic Engineering, 2018, 45(4): 170654. DOI:10.12086/oee.2018.170654
奚玉鼎, 于涌, 丁媛媛, 等.一种快速搜索空中低慢小目标的光电系统[J].光电工程, 2018, 45(4): 170654. DOI:10.12086/oee.2018.170654
9 Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012: 1097–1105.
10 Chatfield K, Simonyan K, Vedaldi A, et al. Return of the devil in the details: delving deep into convolutional nets[J]. arXiv: 1405.3531[cs.CV], 2014.
11 Hyeonseob N, Mooyeol B, Bohyung H. Modeling and Propagating CNNs in a Tree Structure for Visual Tracking[J]. arXiv: 1608.07242v1[cs.CV], 2016: 1–10.
12 Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640–651. DOI:10.1109/TPAMI.2016.2572683
13 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 Conference on Computer Vision and Pattern Recognition, 2014: 580–587.
14 Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional Siamese networks for object tracking[C]//Proceedings of 2016 European Conference on Computer Vision, 2016: 850–865.
15 Valmadre J, Bertinetto L, Henriques J F, et al. End-to-end representation learning for Correlation Filter based tracking[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
16 Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of 2016 IEEE Conference onComputer Vision and Pattern Recognition, 2016: 4293–4302.
17 Held D, Thrun S, Savarese S. Learning to track at 100 FPS with deep regression networks[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 745–765.
18 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv: 1409.1556[cs.CV], 2014.
19 Chen K, Tao W B. Once for all: a two-flow convolutional neural network for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(12): 3377–3386. DOI:10.1109/TCSVT.2017.2757061
20 Leal-Taixé L, Canton-Ferrer C, Schindler C. Learning by tracking: Siamese CNN for robust target association[C]//Proceedings of 2016 Computer Vision and Pattern Recognition Workshops, 2016: 418–425.
21 Tao R, Gavves E, Smeulders A W M. Siamese instance search for tracking[C]//Proceedings of 2016 IEEE Conference onComputer Vision and Pattern Recognition, 2016: 1420–1429.
22 Wang N Y, Li S Y, Gupta A, et al. Transferring rich feature hierarchies for robust visual tracking[J]. arXiv: 1501.04587[cs.CV], 2015.
23 Zhai M Y, Roshtkhari M J, Mori G. Deep learning of appearance models for online object tracking[J]. arXiv: 1607.02568[cs.CV], 2016.
24 Wang H Y, Yang Y T, Zhang Z, et al. Deep-learning-aided multi-pedestrian tracking algorithm[J]. Journal of Image and Graphics, 2017, 22(3): 349–357.
Major Special Fund (G158207)
Get Citation: Zhao Chunmei, Chen Zhongbi, Zhang Jianlin. Application of aircraft target tracking based on deep learning[J]. Opto-Electronic Engineering, 2019, 46(9): 180261.
Previous: Effect of pre-wetting on the wettability of laser ablated Al superhydrophobic/superhydrophilic surface