[1]XIE Jiayang,WANG Xingjian,SHI Zhiguo,et al.Drone detection and tracking in dynamic pan-tilt-zoom cameras[J].CAAI Transactions on Intelligent Systems,2021,16(5):858-869.[doi:10.11992/tis.202103032]
Copy

Drone detection and tracking in dynamic pan-tilt-zoom cameras

References:
[1] SUN Yuyi, CHEN Jiming, HE Shibo, et al. High-confidence gateway planning and performance evaluation of a hybrid LoRa network[J]. IEEE internet of things journal, 2021, 8(2): 1071-1081.
[2] FLOREANO D, WOOD R J. Science, technology and the future of small autonomous drones[J]. Nature, 2015, 521(7553): 460-466.
[3] SHI Xiufang, YANG Chaoqun, XIE Weige, et al. Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges[J]. IEEE communications magazine, 2018, 56(4): 68-74.
[4] XIE Jiayang, YU Jin, WU Junfeng, et al. Adaptive switching spatial-temporal fusion detection for remote flying drones[J]. IEEE transactions on vehicular technology, 2020, 69(7): 6964-6976.
[5] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005: 886-893.
[6] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[7] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 21-37.
[8] LAW H, DENG Jia. CornerNet: detecting objects as paired keypoints[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany, 2018: 765-781.
[9] DUAN Kaiwen, BAI Song, XIE Lingxi, et al. CenterNet: keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South), 2019: 6568-6577.
[10] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK, 2020: 213-229.
[11] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 580-587.
[12] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International journal of computer vision, 2004, 59(2): 167-181.
[13] UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2): 154-171.
[14] ENDRES I, HOIEM D. Category independent object proposals[C]//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010: 575-588.
[15] ZITNICK C L, DOLLáR P. Edge boxes: locating object proposals from edges[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland, 2014: 391-405.
[16] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Quebec, Canada, 2015: 91-99.
[17] CANZIANI A, PASZKE A, CULURCIELLO E. An analysis of deep neural network models for practical applications[J]. arXiv preprint arXiv: 1605.07678, 2016.
[18] TAN Mingxing, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C]//Proceedings of the 36th International Conference on Machine Learning. Long Beach, USA, 2019: 6105-6114.
[19] ZHANG Lei, XU Ke, YU Shiqi, et al. An effective approach for active tracking with a PTZ camera[C]//Proceedings of IEEE International Conference on Robotics and Biomimetics. Tianjin, China, 2010: 1768-1773.
[20] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 2544-2550.
[21] 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.
[22] DANELLJAN M, H?GER G, KHAN F S, et al. Discriminative scale space tracking[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(8): 1561-1575.
[23] DANELLJAN M, ROBINSON A, KHAN F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 472-488.
[24] DANELLJAN M, BHAT G, SHAHBAZ KHAN F, et al. ECO: efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 6931-6939.
[25] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]//Proceedings of the European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 850-865.
[26] LI Bo, YAN Junjie, WU Wei, et al. High performance visual tracking with Siamese region proposal network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 8971-8980.
[27] WANG Qiang, ZHANG Li, BERTINETTO L, et al. Fast online object tracking and segmentation: a unifying approach[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019: 1328-1338.
[28] DéNIZ O, BUENO G, BERMEJO E, et al. Fast and accurate global motion compensation[J]. Pattern recognition, 2011, 44(12): 2887-2901.
[29] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]//Proceedings of International Conference on Computer Vision. Barcelona, Spain, 2011: 2564-2571.
[30] FISCHLER M A, BOLLES R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
[31] BRUTZER S, H?FERLIN B, HEIDEMANN G. Evaluation of background subtraction techniques for video surveillance[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA, 2011: 1937 -1944.
[32] CHENG Yuanhang, WANG Jing. A motion image detection method based on the inter-frame difference method[J]. Applied mechanics and materials, 2014, 490: 1283-1286.
[33] SERRA J, VINCENT L. An overview of morphological filtering[J]. Circuits, systems and signal processing, 1992, 11(1): 47-108.
[34] GRAHAM R L, YAO F F. Finding the convex hull of a simple polygon[J]. Journal of algorithms, 1983, 4(4): 324-331.
[35] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 7132-7141.
[36] GHIASI G, LIN T Y, LE Q V. DropBlock: a regularization method for convolutional networks[C]//Proceedings of 33nd Conference on Neural Information Processing Systems. Montréal, Canada, 2018: 10750-10760.
[37] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 32(9): 1627-1645.
[38] HOERL A E, KANNARD R W, BALDWIN K F. Ridge regression: some simulations[J]. Communications in statistics, 1975, 4(2): 105-123.
[39] SCH?LKOPF B, SMOLA A J. Learning with kernels: support vector machines, regularization, optimization, and beyond[M]. Cambridge: MIT Press, 2002.
[40] GRAY R M. Toeplitz and circulant matrices: a review[J]. Foundations and trends? in communications and information theory, 2006, 2(3): 155-239.
[41] KIM Y, BANG H. Introduction to Kalman filter and its applications[M].GOVAERS F. Introduction and Implementations of the Kalman Filter. IntechOpen, 2019.
[42] GOYETTE N, JODOIN P M, PORIKLI F, et al. Changedetection.net: a new change detection benchmark dataset[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA, 2012: 1-8.
[43] ALLEBOSCH G, DEBOEVERIE F, VEELAERT P, et al. EFIC: edge based foreground background segmentation and interior classification for dynamic camera viewpoints[C]//Proceedings of the 16th International Conference on Advanced Concepts for Intelligent Vision Systems. Catania, Italy, 2015: 130-141.
[44] JIANG Shengqin, LU Xiaobo. WeSamBE: a weight-sample-based method for background subtraction[J]. IEEE transactions on circuits and systems for video technology, 2018, 28(9): 2105-2115.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems