Object Tracking and Recapture Model Based on Deep Detection Network Under Airborne Platform

Xu SHEN; Wei MENG; Xiaohui CHENG; Xinzheng WANG

[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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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.