Infrared Technology, Volume. 42, Issue 7, 624(2020)
Object Tracking and Recapture Model Based on Deep Detection Network Under Airborne Platform
[2] [2] KONG L, HUANG D, QIN J, et al. A Joint Framework for Athlete Tracking and Action Recognition in Sports Videos[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 23(5): 1-21.
KONG L, HUANG D, QIN J, et al. A Joint Framework for Athlete Tracking and Action Recognition in Sports Videos[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 23(5): 1-21.
[3] [3] YANG H, WEN J, WU X J, et al. An Efficient Edge Artificial Intelligence Multi-pedestrian Tracking Method with Rank Constraint[J]. IEEE Transactions on Industrial Informatics, 2019, 12(34): 1-1.
YANG H, WEN J, WU X J, et al. An Efficient Edge Artificial Intelligence Multi-pedestrian Tracking Method with Rank Constraint[J]. IEEE Transactions on Industrial Informatics, 2019, 12(34): 1-1.
[4] [4] MA Chao, YANG M, ZHAN C M, et al. Hierarchical features for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017, 48(9): 21-31.
MA Chao, YANG M, ZHAN C M, et al. Hierarchical features for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017, 48(9): 21-31.
[6] [6] Poppenk J, Norman K A . Multiple-object Tracking as a Tool for Parametrically Modulating Memory Reactivation[J]. Journal of Cognitive Neuroscience, 2017: 1-15.
Poppenk J, Norman K A . Multiple-object Tracking as a Tool for Parametrically Modulating Memory Reactivation[J]. Journal of Cognitive Neuroscience, 2017: 1-15.
[7] [7] Galteri L, Seidenari L, Bertini M, et al. Spatio-Temporal Closed-Loop Object Detection[J]. IEEE Transactions on Image Processing, 2017, 26(3):1253-1263.
Galteri L, Seidenari L, Bertini M, et al. Spatio-Temporal Closed-Loop Object Detection[J]. IEEE Transactions on Image Processing, 2017, 26(3):1253-1263.
[8] [8] WANG N, YEUNG D Y. Learning a Deep Compact Image Representation for Visual Tracking[C]//Advances in Neural Information Processing Systems, 2013, 38(18): 5045-5068.
WANG N, YEUNG D Y. Learning a Deep Compact Image Representation for Visual Tracking[C]//Advances in Neural Information Processing Systems, 2013, 38(18): 5045-5068.
[9] [9] WANG L, OUYANG Wanli, WANG Xiaogang, et al. Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015, 125(1-3): 3-18.
WANG L, OUYANG Wanli, WANG Xiaogang, et al. Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015, 125(1-3): 3-18.
[10] [10] Bertinetto L, Valmadre J, Henriques J F, et al. Fully-Convolutional Siamese Networks for Object Tracking[C]//European Conference on Computer Vision. Springer, 2016: 850-865.
Bertinetto L, Valmadre J, Henriques J F, et al. Fully-Convolutional Siamese Networks for Object Tracking[C]//European Conference on Computer Vision. Springer, 2016: 850-865.
[11] [11] LUO H, XU L, HUI B, et al. Status and prospect of target tracking based on deep learning[J]. Infrared & Laser Engineering, 2017, 46(5): 502002.
LUO H, XU L, HUI B, et al. Status and prospect of target tracking based on deep learning[J]. Infrared & Laser Engineering, 2017, 46(5): 502002.
[13] [13] WU Y, LIM J, YANG M H. Online Object Tracking: A Benchmark[C]//Computer Vision and Pattern Recognition (CVPR),2013: 019-27.
WU Y, LIM J, YANG M H. Online Object Tracking: A Benchmark[C]//Computer Vision and Pattern Recognition (CVPR),2013: 019-27.
[14] [14] WANG P, CHEN P, YE Y, et al. Understanding Convolution for Semantic Segmentation[C]//IEEE Winter Conference on Applications of Computer Vision, 2018: 1287-1299.
WANG P, CHEN P, YE Y, et al. Understanding Convolution for Semantic Segmentation[C]//IEEE Winter Conference on Applications of Computer Vision, 2018: 1287-1299.
[15] [15] Held D, Thrun S, Savarese S. Learning to Track at 100 FPS with Deep Regression Networks[C]// European Conference on Computer Vision,2016: 29-37.
Held D, Thrun S, Savarese S. Learning to Track at 100 FPS with Deep Regression Networks[C]// European Conference on Computer Vision,2016: 29-37.
[16] [16] Erickson K J, Hanna P M, Westerkamp L A, et al. Evaluation of the VIVID confirmatory identification module[C]//Proc. SPIE, 2007, 6566:65660B-65660B-12.
Erickson K J, Hanna P M, Westerkamp L A, et al. Evaluation of the VIVID confirmatory identification module[C]//Proc. SPIE, 2007, 6566:65660B-65660B-12.
[17] [17] Carvalho T, De Rezende E R S, Alves M T P, et al. Exposing computer generated images by eye’s region classification via transfer learning of VGG19 CNN[C]//16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017: 866-870.
Carvalho T, De Rezende E R S, Alves M T P, et al. Exposing computer generated images by eye’s region classification via transfer learning of VGG19 CNN[C]//16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017: 866-870.
[18] [18] Akiba T, Suzuki S, Fukuda K. Extremely large minibatch SGD: training resnet-50 on imagenet in 15 minutes[J]. arXiv preprint arXiv, 2017,1711: 04325.
Akiba T, Suzuki S, Fukuda K. Extremely large minibatch SGD: training resnet-50 on imagenet in 15 minutes[J]. arXiv preprint arXiv, 2017,1711: 04325.
[19] [19] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks BT[J]. Comput. Vis ECCV, 2014: 818-833.
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks BT[J]. Comput. Vis ECCV, 2014: 818-833.
[20] [20] TAO R, Gavves E, Smeulders A W M . Siamese Instance Search for Tracking[C]//Conference on Computer Vision and Pattern Recognition(CVPR), 2016: 1063-6919.
TAO R, Gavves E, Smeulders A W M . Siamese Instance Search for Tracking[C]//Conference on Computer Vision and Pattern Recognition(CVPR), 2016: 1063-6919.
[21] [21] Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J].Computer Vision and Pattern Recognition, 2018, 12(7): 64-78.
Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J].Computer Vision and Pattern Recognition, 2018, 12(7): 64-78.
[22] [22] WANG Q, GAO J, XING J, et al. DCFnet: Discriminant correlation filters network for visual tracking[J]. Computer Vision and Pattern Recognition, 2017, 1704: 04057.
WANG Q, GAO J, XING J, et al. DCFnet: Discriminant correlation filters network for visual tracking[J]. Computer Vision and Pattern Recognition, 2017, 1704: 04057.
[23] [23] Valmadre J, Bertinetto L, Henriques J F, et al. End-to-end representation learning for correlation filter based tracking[C]//Proc. IEEE Conf Comput. Vis. Pattern Recognit., 2017: 5000-5008.
Valmadre J, Bertinetto L, Henriques J F, et al. End-to-end representation learning for correlation filter based tracking[C]//Proc. IEEE Conf Comput. Vis. Pattern Recognit., 2017: 5000-5008.
[24] [24] LI B, YAN J, WU W, et al. High performance tracking with Siamese region proposal network[C]//Proc. IEEE Comput. Vis.pattern Reconit.(CVPR), 2018: 8971-8980.
LI B, YAN J, WU W, et al. High performance tracking with Siamese region proposal network[C]//Proc. IEEE Comput. Vis.pattern Reconit.(CVPR), 2018: 8971-8980.
[25] [25] Uzkent B, Seo Y W. EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking[C]//IEEE Winter Conference on Applications of Computer Vision, 2018: 77-89.
Uzkent B, Seo Y W. EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking[C]//IEEE Winter Conference on Applications of Computer Vision, 2018: 77-89.
Get Citation
Copy Citation Text
SHEN Xu, MENG Wei, CHENG Xiaohui, WANG Xinzheng. Object Tracking and Recapture Model Based on Deep Detection Network Under Airborne Platform[J]. Infrared Technology, 2020, 42(7): 624
Category:
Received: Aug. 27, 2019
Accepted: --
Published Online: Aug. 18, 2020
The Author Email:
CSTR:32186.14.