[1] Lowell J R. Military applications of localization, tracking, and targeting[J]. IEEE Wireless Communications, 2011, 18(2): 6065.
[2] Mazor E, Averbuch A, Dayan J, et al. Interacting multiple model methods in target tracking: a survey[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 103123.
[3] Danelljan M, Robinson A, Khan F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[C]. Amsterdam: European Conference on Computer Vision, 2016.
[4] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Columbus: IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[5] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]. Las Vegas: IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[6] Harjoseputro Y, Yuda I P, Danukusumo K P. MobileNets: efficient convolutional neural network for identification of protected birds[J]. International Journal on Advanced Science Engineering and Information Technology, 2020, 10(6): 2290.
[7] Liu S, Li X, Gao M, et al. Embedded online fish detection and tracking system via YOLOv3 and parallel correlation filter[C]. Charleston: IEEE Conference on Oceans Mts, 2018.
[8] Asha C S, Narasimhadhan A V. Vehicle counting for traffic management system using YOLO and correlation filter[C]. Bangalore: IEEE International Conference on Electronics, Computing and Communication Technologies, 2018.
[9] Dai W, Jin L, Li G, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. OptoElectronic Engineering, 2018, 45(12): 12086.
[10] Zeiler M, Fergus R. Visualizing and understanding convolutional networks[C]. Zurich: European Conference on Computer Vision, 2014.
[11] Deng L, Yu D. Deep learning: methods and applications[J]. Foundations & Trends in Signal Processing, 2014, 7(3): 10821091.
[12] Shin H C, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging, 2016, 35(5): 12851298.
[13] Lecun Y, Bottou L. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 22782324.
[14] Gao Z, Wang L, Zhou L, et al. HEp2 cell image classification with deep convolutional neural networks[J]. IEEE Journal of Biomedical & Health Informatics, 2017, 21(2): 416428.
[15] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. Boston: IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[16] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]. Las Vegas: IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[17] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Las Vegas: IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[18] Huang G, Liu Z, Laurens V, et al. Densely connected convolutional networks[C]. Honolulu: IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[19] Wang C Y, Liao H, Yeh I H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]. Seattle: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2020.