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Cited by 421Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsUAV Applications and Optimization

Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey

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Wu, Xin, Li, Wei, Hong, Danfeng, Tao, Ran, Du, Qian·2022·IEEE Geoscience and Remote Sensing Magazine
DOI 10.1109/mgrs.2021.3115137
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Abstract

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.

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  • Drones can quickly scan a large area and identify objects of interest such as people, vehicles, or buildings in search and rescue missions.

    “This can be particularly useful in search and rescue missions, where drones can quickly scan a large area and identify objects of interest such as people, vehicles, or buildings [95] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [95]

    The Introduction (p. 1) says UAVs can flexibly collect video data without geographic restriction, and the detection-speed section (p. 6) emphasizes rapid image processing. The benchmark-dataset section (pp. 10–12) includes pedestrians, vehicles, and buildings as detected object types. Our copy does not establish that drones quickly scan large areas to identify those objects in search-and-rescue missions. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • AI and ML algorithms can be used to identify and classify objects in drone imagery.

    “AI and ML algorithms can be used to identify and classify objects in drone imagery.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [95]

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  1. [1][1] M. Shahbazi, J. Th´ eau, and P. M´ enard, 'Recent applications of unmanned aerial imagery in natural resource management,' GISci. Remote Sens. , vol. 51, no. 4, pp. 339-365, 2014. -
  2. [2][2] H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, 'Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges,' IEEE Access , vol. 7, pp. 48572-48634, 2019. -
  3. [3][3] M. Shahbazi, J. Th´ eau, and P. M´ enard, 'Recent applications of unmanned aerial imagery in natural resource management,' GISci. Remote Sens. , vol. 51, no. 4, pp. 339-365, 2014. -
  4. [4][4] D. Hong, N. Yokoya, N. Ge, J. Chanussot, and X. Zhu, 'Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification,' ISPRS J. Photogramm. Remote Sens. , vol. 147, pp. 193-205, 2019. -
  5. [5][5] D. Hong, L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, and B. Zhang, 'More diverse means better: Multimodal deep learning meets remote-sensing imagery classification,' IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 5, pp. 4340-4354, 2021. -
  6. [6][6] E. Honkavaara, H. Saari, J. K. nd I. P¨ ol¨ onen, T. Hakala, P. Litkey, J. M¨ akynen, and L. Pesonen, 'Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight uav spectral camera for precision agriculture,' Remote Sens. , vol. 5, no. 10, pp. 5006-5039, 2013. -
  7. [7][7] R. Francisco, Q. Zhang, and J. Reid, 'Stereo vision three-dimensional terrain maps for precision agriculture,' Comput. Electron. Agr. , vol. 60, no. 2, pp. 133-143, 2008. -
  8. [8][8] H. Menouar, I. Guvenc, K. Akkaya, A. S. Uluagac, A. Kadri, and A. Tuncer, 'Uav-enabled intelligent transportation systems for the smart city: Applications and challenges,' IEEE Commun. Mag. , vol. 55, no. 3, pp. 22-28, 2017. -
  9. [9][9] E. Honkavaara, H. Saari, J. Kaivosoja, I. P¨ ol¨ onen, T. Hakala, P. Litkey, J. M¨ akynen, and L. Pesonen, 'Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight uav spectral camera for precision agriculture,' Remote Sens. , vol. 5, no. 10, pp. 5006-5039, 2013. -
  10. [10][10] M. Erdelj, E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, 'Help from the sky: Leveraging uavs for disaster management,' IEEE Pervasive Comput. , vol. 16, no. 1, pp. 24-32, 2017. -
  11. [11][11] L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian, 'Statistical modeling of complex backgrounds for foreground object detection,' IEEE Trans. Image Process. , vol. 13, no. 11, pp. 1459-1472, 2004. -
  12. [12][12] M. Haag and H.-H. Nagel, 'Combination of edge element and optical flow estimates for 3d-model-based vehicle tracking in traffic image sequences,' Int. J. Comput. Vis. , vol. 35, no. 3, pp. 295-319, 1999. -
  13. [13][13] B. Rasti, D. Hong, R. Hang, P. Ghamisi, X. Kang, J. Chanussot, and J. Benediktsson, 'Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox,' IEEE Geosci. Remote Sens. Mag. , vol. 8, no. 4, pp. 60-88, 2020. -
  14. [14][14] X. Wu, W. Li, D. Hong, J. Tian, R. Tao, and Q. Du, 'Vehicle detection of multi-source remote sensing data using active fine-tuning network,' ISPRS J. Photogramm. Remote Sens. , vol. 167, pp. 39-53, 2020. -
  15. [15][15] D. Hong, L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot, 'Graph convolutional networks for hyperspectral image classification,' IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 7, pp. 5966-5978, 2021. -
  16. [16][16] M. Liu, X. Wang, A. Zhou, X. Fu, Y. Ma, and C. Piao, 'Uav-yolo: Small object detection on unmanned aerial vehicle perspective,' Sensor , vol. 20, no. 8, p. 2238, 2020. -
  17. [17][17] Y. Cai, D. Du, L. Zhang, L. Wen, W. Wang, Y. Wu, and S. Lyu, 'Guided attention network for object detection and counting on drones,' arXiv preprint arXiv:1909.11307 , 2019. - · arXiv
  18. [18][18] R. Girshick, J. Donahue, T. Darrell, and J. Malik, 'Rich feature hierarchies for accurate object detection and semantic segmentation,' in Proc. CVPR , pp. 580-587, 2014. -
  19. [19][19] R. Girshick, 'Fast r-cnn,' in Proc. ICCV , pp. 1440-1448, 2015. -
  20. [21][21] P. Zhu, L. Wen, X. Bian, H. Ling, and Q. Hu, 'Vision meets drones: A challenge,' arXiv preprint arXiv:1804.07437 , 2018. - · arXiv
  21. [22][22] Z. Deng, H. Sun, S. Zhou, J. Zhao, L. Lei, and H. Zou, 'Multi-scale object detection in remote sensing imagery with convolutional neural networks,' ISPRS J. Photogramm. Remote Sens. , vol. 145, pp. 3-22, 2018. -
  22. [23][23] X. Wu, D. Hong, P. Ghamisi, W. Li, and R. Tao, 'Msri-ccf: Multi-scale and rotation-insensitive convolutional channel features for geospatial object detection,' Remote Sens. , vol. 10, no. 12, p. 1990, 2018. -
  23. [24][24] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. Berg, 'SSD: Single shot multibox detector,' in Proc. ECCV , pp. 2137, Springer, 2016. -
  24. [25][25] X. Luo, X. Tian, H. Zhang, W. Hou, G. Leng, W. Xu, H. Jia, X. He, M. Wang, and J. Zhang, 'Fast automatic vehicle detection in uav images using convolutional neural networks,' Remote Sens. , vol. 12, no. 12, p. 1994, 2020. -
  25. [27][27] A. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, 'Mobilenets: Efficient convolutional neural networks for mobile vision applications,' arXiv preprint arXiv:1704.04861 , 2017. - · arXiv
  26. [28][28] J. Redmon and A. Farhadi, 'Yolov3: An incremental improvement,' arXiv preprint arXiv:1804.02767 , 2018. - · arXiv
  27. [29][29] S. Mehta, M. Rastegari, L. Shapiro, and H. Hajishirzi, 'Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network,' in Proc. CVPR , pp. 9190-9200, 2019. -
  28. [31][31] D. Hong, N. Yokoya, J. Chanussot, and X. X. Zhu, 'CoSpace: Common subspace learning from hyperspectral-multispectral correspondences,' IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 7, pp. 4349-4359, 2019. -
  29. [32][32] D. Hong, N. Yokoya, J. Chanussot, and X. X. Zhu, 'An augmented linear mixing model to address spectral variability for hyperspectral unmixing,' IEEE Trans. Image Process. , vol. 28, no. 4, pp. 1923-1938, 2019. -
  30. [33][33] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ ar, and L. Zitnick, 'Microsoft coco: Common objects in context,' in Proc. ECCV , pp. 740-755, Springer, 2014. -
  31. [34][34] J. Han, D. Zhang, G. Cheng, N. Liu, and D. Xu, 'Advanced deeplearning techniques for salient and category-specific object detection: a survey,' IEEE Signal Process. Mag. , vol. 35, no. 1, pp. 84-100, 2018. -
  32. [35][35] K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, 'Object detection in optical remote sensing images: A survey and a new benchmark,' ISPRS J. Photogramm. Remote Sens. , vol. 159, pp. 296-307, 2020. -
  33. [36][36] T. Z. Xiang, G. S. Xia, and L. Zhang, 'Mini-uav-based remote sensing: Techniques, applications and prospectives,' IEEE GEOSC REM SEN M. , vol. 7, no. 3, pp. 29-63, 2018. -
  34. [37][37] H. Yao, R. Qin, and X. Chen, 'Unmanned aerial vehicle for remote sensing applications-a review,' Remote Sens , vol. 11, no. 12, 2019. -
  35. [38][38] N. Yin, R. Liu, B. Zeng, and N. Liu, 'A review: Uav-based remote sensing,' in Proc. IOP , vol. 490, p. 062014, IOP Publishing, 2019. -
  36. [39][39] L. P. Osco, J. M. Junior, A. P. M. Ramos, L. A. d. C. Jorge, S. N. Fatholahi, J. d. A. Silva, E. T. Matsubara, H. Pistori, W. N. Gonc ¸alves, and J. Li, 'A review on deep learning in uav remote sensing,' arXiv preprint arXiv:2101.10861 , 2021. - · arXiv
  37. [42][42] A. Ayalew and Pooja, 'A review on object detection from unmanned aerial vehicle using cnn,' IJARIIT , vol. 5, pp. 241-243, 2019. -
  38. [43][43] D. Cazzato, C. Cimarelli, J. L. Sanchez-Lopez, H. Voos, and M. Leo, 'A survey of computer vision methods for 2d object detection from unmanned aerial vehicles,' Journal of Imaging , vol. 6, no. 8, 2020. -
  39. [44][44] A. Ammar, A. Koubaa, M. Ahmed, A. Saad, and B. Benjdira, 'Vehicle detection from aerial images using deep learning: A comparative study,' Electronics , vol. 10, no. 7, 2021. -
  40. [45][45] J. Hao, Y. Zhou, G. Zhang, Q. Lv, and Q. Wu, 'A review of target tracking algorithm based on uav,' in Proc. CBS , pp. 328-333, IEEE, 2018. -
  41. [46][46] G. Pajares, 'Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs),' Photogramm. Eng. Remote Sens. , vol. 81, no. 4, pp. 281-330, 2015. -
  42. [47][47] T. Ad˜ ao, J. Hruˇ ska, L. P´ adua, J. Bessa, E. Peres, R. Morais, and J. Sousa, 'Hyperspectral imaging: A review on uav-based sensors, data processing and applications for agriculture and forestry,' Remote Sens, , vol. 9, no. 11, p. 1110, 2017. -
  43. [48][48] Y. Chen, Y. Zhang, J. Xin, G. Wang, L. Mu, Y. Yi, H. Liu, and D. Liu, 'UAV image-based forest fire detection approach using convolutional neural network,' in Proc. ICIEA , pp. 2118-2123, June 2019. -
  44. [49][49] T. Xiang, G. Xia, and L. Zhang, 'Mini-UAV-based remote sensing: techniques, applications and prospectives,' arXiv preprint arXiv:1812.07770 , 2018. - · arXiv
  45. [50][50] X. Chen, S. Xiang, C. Liu, and C. Pan, 'Vehicle detection in satellite images by hybrid deep convolutional neural networks,' IEEE Geosci. Remote Sens. Lett. , vol. 11, no. 10, pp. 1797-1801, 2014. -
  46. [51][51] X. Liu, C. Deng, J. Chanussot, D. Hong, and B. Zhao, 'Stfnet: A twostream convolutional neural network for spatiotemporal image fusion,' IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 9, pp. 6552-6564, 2019. -
  47. [52][52] Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos, 'A unified multi-scale deep convolutional neural network for fast object detection,' in Proc. ECCV , pp. 354-370, Springer, 2016. -
  48. [53][53] C. Chen, Y. Zhang, Q. Lv, S. Wei, X. Wang, X. Sun, and J. Dong, 'Rrnet: A hybrid detector for object detection in drone-captured images,' in Proc. ICCV Workshops , pp. 0-0, 2019. -
  49. [54][54] Q. Lin, Y. Ding, H. Xu, W. Lin, J. Li, and X. Xie, 'Ecascade-rcnn: Enhanced cascade rcnn for multi-scale object detection in uav images,' in Proc. ICARA , pp. 268-272, 2021. -
  50. [55][55] Z. Li, X. Liu, Y. Zhao, B. Liu, Z. Huang, and R. Hong, 'A lightweight multi-scale aggregated model for detecting aerial images captured by uavs,' J. Vis. Commun. Image Represent. , vol. 77, p. 103058, 2021. -
  51. [56][56] W. Zhang, C. Liu, F. Chang, and Y. Song, 'Multi-scale and occlusion aware network for vehicle detection and segmentation on uav aerial images,' Remote Sens. , vol. 12, no. 11, p. 1760, 2020. -
  52. [58][58] D. R. Pailla, V. Kollerathu, and S. S. Chennamsetty, 'Object detection on aerial imagery using centernet,' arXiv preprint arXiv:1908.08244 , 2019. - · arXiv
  53. [59][59] J. Yang, X. Xie, and W. Yang, 'Effective contexts for uav vehicle detection,' IEEE Access , vol. 7, pp. 85042-85054, 2019. -
  54. [60][60] C. Chen, W. Gong, Y. Chen, and W. Li, 'Object detection in remote sensing images based on a scene-contextual feature pyramid network,' Remote Sens. , vol. 11, no. 3, p. 339, 2019. -
  55. [61][61] R. Zhang, Z. Shao, X. Huang, J. Wang, and D. Li, 'Object detection in uav images via global density fused convolutional network,' Remote Sens. , vol. 12, no. 19, p. 3140, 2020. -
  56. [63][63] Y. Liu, Z. Ding, Y. Cao, and M. Chang, 'Multi-scale feature fusion uav image object detection method based on dilated convolution and attention mechanism,' in Proc. ICIT , pp. 125-132, 2020. -
  57. [64][64] S. Xie, R. Girshick, P. Doll´ ar, Z. Tu, and K. He, 'Aggregated residual transformations for deep neural networks,' in Proc. CVPR , pp. 14921500, 2017. -
  58. [65][65] P. Zhang, Y. Zhong, and X. Li, 'Slimyolov3: Narrower, faster and better for real-time uav applications,' in Proc. ICCV , pp. 0-0, 2019. -
  59. [66][66] X. Liang, J. Zhang, L. Zhuo, Y. Li, and Q. Tian, 'Small object detection in unmanned aerial vehicle images using feature fusion and scalingbased single shot detector with spatial context analysis,' IEEE Trans. Circuits Syst. Video Technol. , vol. 30, no. 6, pp. 1758-1770, 2019. -
  60. [67][67] H. Wang, Z. Wang, M. Jia, A. Li, T. Feng, W. Zhang, and L. Jiao, 'Spatial attention for multi-scale feature refinement for object detection,' in Proc. ICCV Workshops , pp. 0-0, 2019. -
  61. [68][68] Q. Wu and Y. Zhou, 'Real-time object detection based on unmanned aerial vehicle,' in Proc. DDCLS , pp. 574-579, 2019. -
  62. [69][69] Z. Wu, K. Suresh, P. Narayanan, H. Xu, H. Kwon, and Z. Wang, 'Delving into robust object detection from unmanned aerial vehicles: A deep nuisance disentanglement approach,' in Proc. ICCV , pp. 12011210, 2019. -
  63. [70][70] Z. Liu, G. Gao, L. Sun, and Z. Fang, 'Hrdnet: high-resolution detection network for small objects,' arXiv preprint arXiv:2006.07607 , 2020. - · arXiv
  64. [71][71] B. M. Albaba and S. Ozer, 'Synet: An ensemble network for object detection in uav images,' arXiv preprint arXiv:2012.12991 , 2020. - · arXiv
  65. [72][72] M. Li, X. Zhao, J. Li, and L. Nan, 'Comnet: Combinational neural network for object detection in uav-borne thermal images,' IEEE Trans. Geosci. Remote. Sens. , 2020. -
  66. [73][73] Y. Liu, F. Yang, and P. Hu, 'Small-object detection in uav-captured images via multi-branch parallel feature pyramid networks,' IEEE Access , vol. 8, pp. 145740-145750, 2020. -
  67. [74][74] J. Cao, H. Cholakkal, R. M. Anwer, F. S. Khan, Y. Pang, and L. Shao, 'D2det: Towards high quality object detection and instance segmentation,' in Proc. CVPR , pp. 11485-11494, 2020. -
  68. [75][75] Z. Zhang, Y. Liu, T. Liu, Z. Lin, and S. Wang, 'Dagn: A real-time uav remote sensing image vehicle detection framework,' IEEE Geosci. Remote Sens. Lett. , vol. 17, no. 11, pp. 1884-1888, 2019. -
  69. [76][76] C. YuanQiang, D. Du, L. Zhang, L. Wen, W. Wang, Y. Wu, and S. Lyu, 'Guided attention network for object detection and counting on drones,' in Proc. ACM MM , pp. 709-717, 2020. -
  70. [77][77] A. Jadhav, P. Mukherjee, V. Kaushik, and B. Lall, 'Aerial multi-object tracking by detection using deep association networks,' in Proc. NCC , pp. 1-6, 2020. -
  71. [78][78] J. Zhang, X. Liang, M. Wang, L. Yang, and L. Zhuo, 'Coarseto-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency,' Neurocomputing , vol. 398, pp. 555-565, 2020. -
  72. [79][79] G. Tian, J. Liu, and W. Yang, 'A dual neural network for object detection in uav images,' Neurocomputing , vol. 443, pp. 292-301, 2021. -
  73. [81][81] W. Yu, T. Yang, and C. Chen, 'Towards resolving the challenge of longtail distribution in uav images for object detection,' in Proc. WACV , pp. 3258-3267, 2021. -
  74. [84][84] Z. Tang, X. Liu, G. Shen, and B. Yang, 'Penet: Object detection using points estimation in aerial images,' arXiv preprint arXiv:2001.08247 , 2020. - · arXiv
  75. [85][85] V. Ruzicka and F. Franchetti, 'Fast and accurate object detection in high resolution 4k and 8k video using gpus,' in Proc. HPEC , pp. 1-7, IEEE, 2018. -
  76. [86][86] G. Plastiras, C. Kyrkou, and T. Theocharides, 'Efficient convnetbased object detection for unmanned aerial vehicles by selective tile processing,' in Proc. ICDSC , pp. 1-6, 2018. -
  77. [87][87] O. Unel, B. Ozkalayci, and C. Cigla, 'The power of tiling for small object detection,' in Proc. CVPR Workshops , June 2019. -
  78. [88][88] M. L. Mekhalfi, M. B. Bejiga, D. Soresina, F. Melgani, and B. Demir, 'Capsule networks for object detection in uav imagery,' Remote Sens. , vol. 11, no. 14, p. 1694, 2019. -
  79. [89][89] G. Cheng, P. Zhou, and J. Han, 'Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images,' IEEE Trans. Geosci. Remote Sens. , vol. 54, no. 12, pp. 74057415, 2016. -
  80. [90][90] D. Laptev, N. Savinov, J. Buhmann, and M. Pollefeys, 'Ti-pooling: transformation-invariant pooling for feature learning in convolutional neural networks,' in Proc. CVPR , pp. 289-297, 2016. -
  81. [91][91] G. Cheng, P. Zhou, and J. Han, 'Rifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection,' in Proc. CVPR , pp. 2884-2893, 2016. -
  82. [93][93] Z. Liu, H. Wang, L. Weng, and Y. Yang, 'Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds,' IEEE Geosci. Remote Sens.Lett. , vol. 13, no. 8, pp. 1074-1078, 2016. -
  83. [94][94] Z. Liu, J. Hu, L. Weng, and Y. Yang, 'Rotated region based cnn for ship detection,' in Proc. ICIP , pp. 900-904, IEEE, 2017. -
  84. [95][95] D. Marcos, M. Volpi, and D. Tuia, 'Learning rotation invariant convolutional filters for texture classification,' in Proc. ICPR , pp. 2012-2017, IEEE, 2016. -
  85. [96][96] K. Li, G. Cheng, S. Bu, and X. You, 'Rotation-insensitive and contextaugmented object detection in remote sensing images,' IEEE Trans. Geosci. Remote Sens. , vol. 56, no. 4, pp. 2337-2348, 2017. -
  86. [97][97] K. Fu, Z. Chang, Y. Zhang, G. Xu, K. Zhang, and X. Sun, 'Rotationaware and multi-scale convolutional neural network for object detection in remote sensing images,' ISPRS J. Photogramm. Remote Sens. , vol. 161, pp. 294-308, 2020. -
  87. [98][98] Y. Yu, H. Guan, D. Li, T. Gu, E. Tang, and A. Li, 'Orientation guided anchoring for geospatial object detection from remote sensing imagery,' ISPRS J. Photogramm. Remote Sens. , vol. 160, pp. 67-82, 2020. -
  88. [99][99] M. Liao, Z. Zhu, B. Shi, G.-s. Xia, and X. Bai, 'Rotation-sensitive regression for oriented scene text detection,' in Proc. CVPR , pp. 59095918, 2018. -
  89. [100][100] M. Liao, B. Shi, and X. Bai, 'Textboxes ++ : A single-shot oriented scene text detector,' IEEE Trans. Image Process. , vol. 27, no. 8, pp. 3676-3690, 2018. -
  90. [101][101] Y. Jiang, X. Zhu, X. Wang, S. Yang, W. Li, H. Wang, P. Fu, and Z. Luo, 'R 2 cnn: Rotational region cnn for arbitrarily-oriented scene text detection,' in Proc. ICPR , pp. 3610-3615, IEEE, 2018. -
  91. [102][102] J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng, and X. Xue, 'Arbitrary-oriented scene text detection via rotation proposals,' IEEE Trans. Multimedia , vol. 20, no. 11, pp. 3111-3122, 2018. -
  92. [104][104] Y. Li, Q. Huang, X. Pei, L. Jiao, and R. Shang, 'Radet: Refine feature pyramid network and multi-layer attention network for arbitraryoriented object detection of remote sensing images,' Remote Sens. , vol. 12, no. 3, p. 389, 2020. -
  93. [105][105] Y. Zhou, Q. Ye, Q. Qiu, and J. Jiao, 'Oriented response networks,' in Proc. CVPR , pp. 519-528, 2017. -
  94. [106][106] C. Esteves, C. Allen-Blanchette, X. Zhou, and K. Daniilidis, 'Polar transformer networks,' arXiv preprint arXiv:1709.01889 , 2017. - · arXiv
  95. [107][107] K. S. Tai, P. Bailis, and G. Valiant, 'Equivariant transformer networks,' arXiv preprint arXiv:1901.11399 , 2019. - · arXiv
  96. [108][108] K. Zhou, Z. Zhang, C. Gao, and J. Liu, 'Rotated feature network for multiorientation object detection of remote-sensing images,' IEEE Geosci. Remote Sensing Lett. , pp. 1-5, 2020. -
  97. [109][109] U. Schmidt and S. Roth, 'Learning rotation-aware features: From invariant priors to equivariant descriptors,' in Proc. CVPR , pp. 20502057, IEEE, 2012. -
  98. [110][110] X. Wu, D. Hong, J. Tian, J. Chanussot, W. Li, and R. Tao, 'ORSIm Detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features,' IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 7, pp. 5146-5158, 2019. -
  99. [111][111] R. J. Wang, X. Li, and C. X. Ling, 'Pelee: A real-time object detection system on mobile devices,' in Proc. NIPS , pp. 1963-1972, 2018. -
  100. [112][112] Y. Yang, G. Xie, and Y. Qu, 'Real-time detection of aircraft objects in remote sensing images based on improved yolov4,' in Proc. IAEAC , vol. 5, pp. 1156-1164, 2021. -
  101. [113][113] X. Wang, W. Li, W. Guo, and K. Cao, 'Spb-yolo: An efficient realtime detector for unmanned aerial vehicle images,' in Prof. ICAIIC , pp. 099-104, 2021. -
  102. [114][114] Z. Pi, Y. Lian, X. Chen, Y. Wu, Y. Li, and L. Jiao, 'A novel spatial and temporal context-aware approach for drone-based video object detection,' in Proc. ICCV Workshops , Oct 2019. -
  103. [115][115] T. Wang, J. Xiong, X. Xu, and Y. Shi, 'Scnn: a general distribution based statistical convolutional neural network with application to video object detection,' in Proc. AAAI , vol. 33, pp. 5321-5328, 2019. -
  104. [116][116] P. Nousi, I. Mademlis, I. Karakostas, A. Tefas, and I. Pitas, 'Embedded uav real-time visual object detection and tracking,' in Proc. RCAR , pp. 708-713, 2019. -
  105. [117][117] K. M. Abughalieh, B. H. Sababha, and N. A. Rawashdeh, 'A videobased object detection and tracking system for weight sensitive uavs,' Multimed. Tools. Appl. , vol. 78, no. 7, pp. 9149-9167, 2019. -
  106. [118][118] Y. Zhang, L. Shen, X. Wang, and H.-M. Hu, 'Drone video object detection using convolutional neural networks with time domain motion features,' in Proc. MIPR , pp. 153-156, 2020. -
  107. [119][119] M. Mandal, L. K. Kumar, and S. K. Vipparthi, 'Mor-uav: A benchmark dataset and baselines for moving object recognition in uav videos,' in Proc. ACM MM , pp. 2626-2635, 2020. -
  108. [120][120] H. Xie and H. Shin, 'Two-stream small-scale pedestrian detection network with feature aggregation for drone-view videos,' Multidim. Syst. Sign. P . , pp. 1-17, 2021. -
  109. [123][123] K. Kang, H. Li, J. Yan, X. Zeng, B. Yang, T. Xiao, C. Zhang, Z. Wang, R. Wang, X. Wang, et al. , 'T-cnn: Tubelets with convolutional neural networks for object detection from videos,' IEEE Trans. Circuits Syst. Video Technol. , vol. 28, no. 10, pp. 2896-2907, 2017. -
  110. [124][124] W. Han, P. Khorrami, T. L. Paine, P. Ramachandran, M. Babaeizadeh, H. Shi, J. Li, S. Yan, and T. S. Huang, 'Seq-nms for video object detection,' arXiv preprint arXiv:1602.08465 , 2016. - · arXiv
  111. [125][125] Q. Zhao, Y. Wang, T. Sheng, and Z. Tang, 'Comprehensive feature enhancement module for single-shot object detector,' in Proc. ACCV , pp. 325-340, Springer, 2018. -
  112. [127][127] K. Sun, B. Xiao, D. Liu, and J. Wang, 'Deep high-resolution representation learning for human pose estimation,' in Proc. CVPR , pp. 56935703, 2019. -
  113. [129][129] X. Zhou, D. Wang, and P. Kr¨ ahenb¨ uhl, 'Objects as points,' arXiv preprint arXiv:1904.07850 , 2019. - · arXiv
  114. [132][132] D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, 'Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,' in Prof. CVPR , pp. 8934-8943, 2018. -
  115. [133][133] A. Ranjan and M. J. Black, 'Optical flow estimation using a spatial pyramid network,' in Prof. CVPR , pp. 2720-2729, 2017. -
  116. [134][134] X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei, 'Deep feature flow for video recognition,' in Proc. CVPR , pp. 2349-2358, 2017. -
  117. [136][136] C. Hetang, H. Qin, S. Liu, and J. Yan, 'Impression network for video object detection,' arXiv preprint arXiv:1712.05896 , 2017. - · arXiv
  118. [137][137] X. Zhu, J. Dai, L. Yuan, and Y. Wei, 'Towards high performance video object detection,' in Proc. CVPR , pp. 7210-7218, 2018. -
  119. [138][138] X. Zhu, J. Dai, X. Zhu, Y. Wei, and L. Yuan, 'Towards high performance video object detection for mobiles,' arXiv preprint arXiv:1804.05830 , 2018. - · arXiv
  120. [139][139] R. Hang, Q. Liu, D. Hong, and P. Ghamisi, 'Cascaded recurrent neural networks for hyperspectral image classification,' IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 8, pp. 5384-5394, 2019. -
  121. [140][140] Y. Lu, C. Lu, and C.-K. Tang, 'Online video object detection using association lstm,' in Proc. ICCV , pp. 2344-2352, 2017. -
  122. [141][141] M. Liu, M. Zhu, M. White, Y. Li, and D. Kalenichenko, 'Looking fast and slow: Memory-guided mobile video object detection,' arXiv preprint arXiv:1903.10172 , 2019. - · arXiv
  123. [142][142] M. Liu and M. Zhu, 'Mobile video object detection with temporallyaware feature maps,' in Proc. CVPR , pp. 5686-5695, 2018. -
  124. [143][143] Z. Jiang, P. Gao, C. Guo, Q. Zhang, S. Xiang, and C. Pan, 'Video object detection with locally-weighted deformable neighbors,' in Proc. AAAI , vol. 33, pp. 8529-8536, 2019. -
  125. [144][144] S. Tripathi, Z. C. Lipton, S. Belongie, and T. Nguyen, 'Context matters: Refining object detection in video with recurrent neural networks,' arXiv preprint arXiv:1607.04648 , 2016. - · arXiv
  126. [145][145] F. Xiao and Y. Jae Lee, 'Video object detection with an aligned spatialtemporal memory,' in Proc. ECCV , pp. 485-501, 2018. -
  127. [146][146] H. Luo, W. Xie, X. Wang, and W. Zeng, 'Detect or track: Towards cost-effective video object detection/tracking,' in Proc. AAAI , vol. 33, pp. 8803-8810, 2019. -
  128. [147][147] N. Wojke, A. Bewley, and D. Paulus, 'Simple online and realtime tracking with a deep association metric,' in Proc. ICIP , pp. 3645-3649, IEEE, 2017. -
  129. [148][148] N. M. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan, 'Multi-object tracking cascade with multi-step data association and occlusion handling,' in Proc. AVSS , pp. 1-6, 2018. -
  130. [149][149] Y. Zhou, T. Rui, Y. Li, and X. Zuo, 'A uav patrol system using panoramic stitching and object detection,' Comput. Electr. Eng. , vol. 80, p. 106473, 2019. -
  131. [150][150] J. Wang, S. Simeonova, and M. Shahbazi, 'Orientationand scaleinvariant multi-vehicle detection and tracking from unmanned aerial videos,' Remote Sens , vol. 11, no. 18, 2019. -
  132. [151][151] W. Li, J. Mu, and G. Liu, 'Multiple object tracking with motion and appearance cues,' in Proc. ICCV Workshops , pp. 0-0, 2019. -
  133. [152][152] H. Zhang, G. Wang, Z. Lei, and J.-N. Hwang, 'Eye in the sky: Dronebased object tracking and 3d localization,' in Proc. ACM MM , pp. 899907, 2019. -
  134. [153][153] S. Pan, Z. Tong, Y. Zhao, Z. Zhao, F. Su, and B. Zhuang, 'Multi-object tracking hierarchically in visual data taken from drones,' in Proc. ICCV Workshops , pp. 0-0, 2019. -
  135. [154][154] T. Yang, D. Li, Y. Bai, F. Zhang, S. Li, M. Wang, Z. Zhang, and J. Li, 'Multiple-object-tracking algorithm based on dense trajectory voting in aerial videos,' Remote Sens. , vol. 11, no. 19, p. 2278, 2019. -
  136. [155][155] H. Ardo and M. Nilsson, 'Multi target tracking from drones by learning from generalized graph differences,' in Proc. ICCV Workshops , pp. 00, 2019. -
  137. [156][156] S. M. Marvasti-Zadeh, J. Khaghani, H. Ghanei-Yakhdan, S. Kasaei, and L. Cheng, 'Comet: context-aware iou-guided network for small object tracking,' in Proc. ACCV , 2020. -
  138. [157][157] H. Yu, G. Li, W. Zhang, H. Yao, and Q. Huang, 'Self-balance motion and appearance model for multi-object tracking in uav,' in Proc. ACM MM Asia , pp. 1-6, 2019. -
  139. [158][158] P. Bergmann, T. Meinhardt, and L. Leal-Taixe, 'Tracking without bells and whistles,' in Proc. ICCV , pp. 941-951, 2019. -
  140. [159][159] H. Yu, G. Li, L. Su, B. Zhong, H. Yao, and Q. Huang, 'Conditional gan based individual and global motion fusion for multiple object tracking in uav videos,' Pattern Recognit. Lett. , vol. 131, pp. 219-226, 2020. -
  141. [160][160] S. Kapania, D. Saini, S. Goyal, N. Thakur, R. Jain, and P. Nagrath, 'Multi object tracking with uavs using deep sort and yolov3 retinanet detection framework,' in Proc. ACM. AIMS , pp. 1-6, 2020. -
  142. [161][161] D. Stadler, L. W. Sommer, and J. Beyerer, 'Pas tracker: position-, appearance-and size-aware multi-object tracking in drone videos,' in Proc. ECCV , pp. 604-620, Springer, 2020. -
  143. [162][162] H. U. Dike and Y. Zhou, 'A robust quadruplet and faster regionbased cnn for uav video-based multiple object tracking in crowded environment,' Electronics , vol. 10, no. 7, p. 795, 2021. -
  144. [163][163] W. Huang, X. Zhou, M. Dong, and H. Xu, 'Multiple objects tracking in the uav system based on hierarchical deep high-resolution network,' Multimed. Tools. Appl. , pp. 1-19, 2021. -
  145. [164][164] S.-H. Bae and K.-J. Yoon, 'Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning,' in Proc. CVPR , pp. 1218-1225, 2014. -
  146. [165][165] Y. Xiang, A. Alahi, and S. Savarese, 'Learning to track: Online multiobject tracking by decision making,' in Proc. ICCV , pp. 4705-4713, 2015. -
  147. [167][167] C. Dicle, O. I. Camps, and M. Sznaier, 'The way they move: Tracking multiple targets with similar appearance,' in Proc. ICCV , pp. 23042311, 2013. -
  148. [168][168] E. Bochinski, V. Eiselein, and T. Sikora, 'High-speed tracking-bydetection without using image information,' in Proc. AVSS , pp. 1-6, IEEE, 2017. -
  149. [169][169] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, 'Simple online and realtime tracking,' in Proc. ICIP , pp. 3464-3468, IEEE, 2016. -
  150. [170][170] A. Milan, S. Roth, and K. Schindler, 'Continuous energy minimization for multitarget tracking,' IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 1, pp. 58-72, 2013. -
  151. [171][171] S. Schulter, P. Vernaza, W. Choi, and M. Chandraker, 'Deep network flow for multi-object tracking,' in Proc. CVPR , pp. 6951-6960, 2017. -
  152. [172][172] J. Son, M. Baek, M. Cho, and B. Han, 'Multi-object tracking with quadruplet convolutional neural networks,' in Proc. CVPR , pp. 56205629, 2017. -
  153. [174][174] A. Jadhav, P. Mukherjee, V. Kaushik, and B. Lall, 'Aerial multi-object tracking by detection using deep association networks,' arXiv preprint arXiv:1909.01547 , 2019. - · arXiv
  154. [175][175] S. Chopra, R. Hadsell, and Y. LeCun, 'Learning a similarity metric discriminatively, with application to face verification,' in Proc. CVPR , vol. 1, pp. 539-546, IEEE, 2005. -
  155. [176][176] J. Lee, S. Kim, and B. C. Ko, 'Online multiple object tracking using rule distillated siamese random forest,' IEEE Access , vol. 8, pp. 182828-182841, 2020. -
  156. [177][177] J. Jin, X. Li, X. Li, and S. Guan, 'Online multi-object tracking with siamese network and optical flow,' in Proc. ICIVC , pp. 193-198, IEEE, 2020. -
  157. [178][178] B. Shuai, A. G. Berneshawi, D. Modolo, and J. Tighe, 'Multi-object tracking with siamese track-rcnn,' arXiv preprint arXiv:2004.07786 , 2020. - · arXiv
  158. [179][179] S.-H. Bae and K.-J. Yoon, 'Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking,' IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 3, pp. 595-610, 2017. -
  159. [180][180] L. Leal-Taix´ e, C. Canton-Ferrer, and K. Schindler, 'Learning by tracking: Siamese cnn for robust target association,' in Proc. CVPR Workshops , pp. 33-40, 2016. -
  160. [181][181] B. Wang, L. Wang, B. Shuai, Z. Zuo, T. Liu, K. Luk Chan, and G. Wang, 'Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association,' in Proc. CVPR Workshops , pp. 1-8, 2016. -
  161. [182][182] A. Li, L. Luo, and S. Tang, 'Real-time tracking of vehicles with siamese network and backward prediction,' in Proc. ICME , pp. 1-6, IEEE, 2020. -
  162. [183][183] X. Yan, X. Wu, I. A. Kakadiaris, and S. K. Shah, 'To track or to detect? an ensemble framework for optimal selection,' in Proc. ECCV , pp. 594-607, Springer, 2012. -
  163. [184][184] Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, and N. Yu, 'Online multi-object tracking using cnn-based single object tracker with spatialtemporal attention mechanism,' in Proc. ICCV , pp. 4836-4845, 2017. -
  164. [185][185] A. Sadeghian, A. Alahi, and S. Savarese, 'Tracking the untrackable: Learning to track multiple cues with long-term dependencies,' in Proc. ICCV , pp. 300-311, 2017. -
  165. [186][186] L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P. H. Torr, 'Staple: Complementary learners for real-time tracking,' in Proc. CVPR , pp. 1401-1409, 2016. -
  166. [187][187] H. Fan and H. Ling, 'Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking,' in Proc. ICCV , pp. 54865494, 2017. -
  167. [188][188] H. Fan and H. Ling, 'Sanet: Structure-aware network for visual tracking,' in Proc. CVPR workshops , pp. 42-49, 2017. -
  168. [189][189] P. Chu, H. Fan, C. C. Tan, and H. Ling, 'Online multi-object tracking with instance-aware tracker and dynamic model refreshment,' in Proc. WACV , pp. 161-170, IEEE, 2019. -
  169. [190][190] W. Feng, Z. Hu, W. Wu, J. Yan, and W. Ouyang, 'Multi-object tracking with multiple cues and switcher-aware classification,' arXiv preprint arXiv:1901.06129 , 2019. - · arXiv
  170. [191][191] B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, 'High performance visual tracking with siamese region proposal network,' in Proc. CVPR , pp. 8971-8980, 2018. -
  171. [192][192] J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, and M.-H. Yang, 'Online multi-object tracking with dual matching attention networks,' in Proc. ECCV , pp. 366-382, 2018. -
  172. [193][193] A. Milan, S. H. Rezatofighi, A. Dick, I. Reid, and K. Schindler, 'Online multi-target tracking using recurrent neural networks,' in Proc. AAAI , pp. 4225-4232, 2017. -
  173. [194][194] Q. Li, X. Zhao, and K. Huang, 'Learning temporally correlated representations using lstms for visual tracking,' in Proc. ICIP , pp. 16141618, IEEE, 2016. -
  174. [195][195] A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, 'Social lstm: Human trajectory prediction in crowded spaces,' in Proc. CVPR , pp. 961-971, 2016. -
  175. [196][196] Y. Liang and Y. Zhou, 'Lstm multiple object tracker combining multiple cues,' in Proc. ICIP , pp. 2351-2355, IEEE, 2018. -
  176. [197][197] C. Kim, F. Li, and J. M. Rehg, 'Multi-object tracking with neural gating using bilinear lstm,' in Proc. ECCV , pp. 200-215, 2018. -
  177. [198][198] H. Meng-Ru, L. Yen-Liang, and H. Winston, 'Drone-based object counting by spatially regularized regional proposal networks,' in Proc. ICCV , IEEE, 2017. -
  178. [199][199] D. Du, Y. Qi, H. Yu, Y. Yang, K. Duan, G. Li, W. Zhang, Q. Huang, and Q. Tian, 'The unmanned aerial vehicle benchmark: Object detection and tracking,' in Proc. ECCV , pp. 370-386, 2018. -
  179. [200][200] X. Xu, X. Zhang, B. Yu, X. S. Hu, C. Rowen, J. Hu, and Y. Shi, 'Dacsdc low power object detection challenge for uav applications,' IEEE Trans. Pattern Anal. Mach. Intell. , 2019. -
  180. [201][201] P. Zhu, L. Wen, D. Du, X. Bian, Q. Hu, and H. Ling, 'Vision meets drones: Past, present and future,' arXiv preprint arXiv:2001.06303 , 2020. - · arXiv
  181. [202][202] P. Zhu, Y. Sun, L. Wen, Y. Feng, and Q. Hu, 'Drone based rgbt vehicle detection and counting: A challenge,' arXiv preprint arXiv:2003.02437 , 2020. - · arXiv
  182. [203][203] I. Bozcan and E. Kayacan, 'Au-air: A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance,' in Proc. ICRA , pp. 8504-8510, IEEE, 2020. -
  183. [204][204] E. Bondi, R. Jain, P. Aggrawal, S. Anand, R. Hannaford, A. Kapoor, J. Piavis, S. Shah, L. Joppa, B. Dilkina, et al. , 'Birdsai: A dataset for detection and tracking in aerial thermal infrared videos,' in Proc. WACV , pp. 1747-1756, 2020. -
  184. [205][205] H. Zhang, M. Sun, Q. Li, L. Liu, M. Liu, and Y. Ji, 'An empirical study of multi-scale object detection in high resolution uav images,' Neurocomputing , vol. 421, pp. 173-182, 2021. -
  185. [206][206] M. Barekatain, M. Mart´ ı, H. Shih, S. Murray, K. Nakayama, Y. Matsuo, and H. Prendinger, 'Okutama-action: An aerial view video dataset for concurrent human action detection,' in Proc. CVPR Workshops , pp. 2835, 2017. -
  186. [207][207] M. Mueller, N. Smith, and B. Ghanem, 'A benchmark and simulator for uav tracking,' in Proc. ECCV , pp. 445-461, Springer, 2016. -
  187. [208][208] S. Li and D. Yeung, 'Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models,' in Proc. AAAI , 2017. -
  188. [209][209] A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, 'Learning social etiquette: Human trajectory understanding in crowded scenes,' in Proc. ECCV , vol. 9912, pp. 549-565, Sep 2016. -
  189. [210][210] W. Zhang, C. Liu, F. Chang, and Y. Song, 'Multi-scale and occlusion aware network for vehicle detection and segmentation on uav aerial images,' Remote Sens. , vol. 12, no. 11, p. 1760, 2020. -
  190. [211][211] H. Law and J. Deng, 'Cornernet: Detecting objects as paired keypoints,' in Proc. ECCV , pp. 734-750, 2018. -
  191. [212][212] S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, 'Single-shot refinement neural network for object detection,' in Proc. CVPR , pp. 4203-4212, 2018. -
  192. [213][213] Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun, 'Detnet: A backbone network for object detection,' arXiv preprint arXiv:1804.06215 , 2018. - · arXiv
  193. [214][214] Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun, 'Lighthead r-cnn: In defense of two-stage object detector,' arXiv preprint arXiv:1711.07264 , 2017. - · arXiv
  194. [215][215] F. Yang, H. Fan, P. Chu, E. Blasch, and H. Ling, 'Clustered object detection in aerial images,' in Proc. ICCV , pp. 8311-8320, 2019. -
  195. [216][216] C. Li, T. Yang, S. Zhu, C. Chen, and S. Guan, 'Density map guided object detection in aerial images,' in Proc. CVPR Workshops , pp. 190191, 2020. -
  196. [217][217] T. Kong, F. Sun, A. Yao, H. Liu, M. Lu, and Y. Chen, 'Ron: Reverse connection with objectness prior networks for object detection,' in Proc. CVPR , pp. 5936-5944, 2017. -
  197. [218][218] P. Zhu, D. Du, L. Wen, X. Bian, H. Ling, Q. Hu, T. Peng, J. Zheng, X. Wang, Y. Zhang, et al. , 'Visdrone-vid2019: The vision meets drone object detection in video challenge results,' in Proc. ICCV Workshops , pp. 0-0, 2019. -
  198. [219][219] Q. Zhao, T. Sheng, Y. Wang, F. Ni, and L. Cai, 'Cfenet: An accurate and efficient single-shot object detector for autonomous driving,' arXiv preprint arXiv:1806.09790 , 2018. - · arXiv
  199. [220][220] P. Zhu, L. Wen, D. Du, X. Bian, H. Ling, Q. Hu, H. Wu, Q. Nie, H. Cheng, C. Liu, et al. , 'Visdrone-vdt2018: The vision meets drone video detection and tracking challenge results,' in Proc. ECCV , pp. 00, 2018. -
  200. [221][221] P. Adarsh, P. Rathi, and M. Kumar, 'Yolo v3-tiny: Object detection and recognition using one stage improved model,' in Proc. ICACCS , pp. 687-694, IEEE, 2020. -

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Authors
Wu, X., Li, W., Hong, D., Tao, R., & Du, Q.
Affiliations
  • Beijing Institute of Technology (CN)
  • Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
  • Mississippi State University (US)
ORCID
Venue
IEEE Geoscience and Remote Sensing Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-03
Volume
10
Issue
1
Pages
91-124
ISSN
  • 2168-6831 (ISSN-L)
  • 2473-2397
  • 2373-7468
DOI
10.1109/mgrs.2021.3115137
OpenAlex
W3210997334
Cited by
421
References
20
Citations by year
2026: 114 · 2025: 105 · 2024: 83 · 2023: 82 · 2022: 36 · 2021: 1
FWCI
17.56
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Geoscience and Remote Sensing Magazine (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (61922013, 62101045, U1833203)
  • China Postdoctoral Science Foundation (2021M690385)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
Keywords
unmanned aerial vehicles, object detection, object tracking, deep learning, remote sensing, environmental monitoring, precision agriculture, traffic management

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