Citation: | ZHANG Tianci, DING Meng, QIAN Xiaoyan, et al. Moving object speed measurement for low-camera-angle surface surveillance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(2): 266-273. doi: 10.13700/j.bh.1001-5965.2019.0234(in Chinese) |
To build an effective airport surface visual surveillance system, a moving object speed measurement method based on long-term feature point tracking and analysis is proposed. First, the surveillance camera is calibrated using geographic features on the airport surface. Then, the feature points in motion regions of the images are continuously tracked via optical flow fields. On this basis, different moving objects are identified by clustering the feature point trajectories. Finally, the speeds of the moving objects are measured according to the heights and moving distances of the feature points. The proposed method can accurately measure the object moving speeds using low-camera-angle monocular video images obtained by cameras installed on the airport surface. Simulation studies are conducted based on the surface operation videos of Guangzhou Baiyun International Airport, which verify the feasibility and advantages of the proposed method for low-camera-angle speed measurement.
[1] |
CERMENO E, PEREZ A, SIGUENZA J A.Intelligent video surveillance beyond robust background modeling[J].Expert Systems with Applications, 2018, 91:138-149. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cf1bff40300ea5ad2ab6d195ef41fee9
|
[2] |
罗晓, 卢宇, 吴宏刚.采用多视频融合的机场场面监视方法[J].电讯技术, 2011, 51(7):128-132. http://d.old.wanfangdata.com.cn/Periodical/dianxjs201107026
LUO X, LU Y, WU H G.A novel airport surface surveillance method using multi-video fusion[J].Telecommunication Engineering, 2011, 51(7):128-132(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/dianxjs201107026
|
[3] |
VIDAKIS D G, KOSMOPOULOS D I.Facilitation of air traffic control via optical character recognition-based aircraft registration number extraction[J].IET Intelligent Transport Systems, 2018, 12(8):965-975. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1d2baa6a05ea7c3c94f1b0e3c16fc45e
|
[4] |
LOPEZ-ARAQUISTAIN J, JARAMA A J, BESADA J A, et al.A new approach to map-assisted bayesian tracking filtering[J].Information Fusion, 2019, 45:79-95. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0b2cb1fc50677c50a154d9504abb714a
|
[5] |
唐勇, 胡明华, 吴洪刚, 等.一种在机场视频中实现飞机自动挂标牌的新方法[J].江苏大学学报(自然科学版), 2013, 34(6):681-686. http://d.old.wanfangdata.com.cn/Periodical/jslgdxxb201306011
TANG Y, HU M H, WU H G, et al.An automatical labeling aircraft method for airport video monitoring[J].Journal of Jiangsu University(Natural Science Edition), 2013, 34(6):681-686(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jslgdxxb201306011
|
[6] |
SAIVADDI V, LU H L.Computer vision based surveillance concept for airport ramp operations[C]//Proceedings of 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference(DASC).Piscataway, NJ: IEEE Press, 2013: 1-35. https://www.researchgate.net/publication/261240874_Computer_vision_based_surveillance_concept_for_airport_ramp_operations
|
[7] |
LU H L, CHENG V H, TSAI J, et al.Airport gate operation monitoring using computer vision techniques[C]//Proceedings of 16th AIAA Aviation Technology, Integration, and Operations Conference.Reston: AIAA, 2016: 1-12. https://www.researchgate.net/publication/303902584_Airport_Gate_Operation_Monitoring_Using_Computer_Vision_Techniques
|
[8] |
DONADIO F, FREJAVILLE J, LARNIER S, et al.Artificial intelligence and collaborative robot to improve airport operations[C]//14th International Conference on Remote Engineering and Virtual Instrumentation(REV).Berlin: Springer, 2018: 973-986. https://www.researchgate.net/publication/319683465_Artificial_Intelligence_and_Collaborative_Robot_to_Improve_Airport_Operations
|
[9] |
LU H L, KWAN J, FONG A, et al.Field testing of vision-based surveillance system for ramp area operations[C]//Proceedings of 2018 Aviation Technology, Integration, and Operations Conference.Reston: AIAA, 2018: 1-11.
|
[10] |
CHEN J, WEISZER M, STEWART P, et al.Toward a more realistic, cost effective and greener ground movement through active routing:Part 1-Optimal speed profile generation[J].IEEE Transactions on Intelligent Transportation Systems, 2016, 17(5):1196-1209. https://ieeexplore.ieee.org/document/7321022/
|
[11] |
KANHERE N K, BIRCHFIELD S T.Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features[J].IEEE Transactions on Intelligent Transportation Systems, 2008, 9(1):148-160. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=42ab67fcdab573aae84d0392a1b1ede6
|
[12] |
詹昭焕, 韩松臣, 李炜, 等.基于倾向流和深度学习的机场运动目标检测[J].交通信息与安全, 2019, 37(1):49-57. http://d.old.wanfangdata.com.cn/Periodical/jtyjsj201901008
ZHAN Z H, HAN S C, LI W, et al.A target detection method of moving objects at airport based on streak flow and deep learning[J].Journal of Transport Information and Safety, 2019, 37(1):49-57(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jtyjsj201901008
|
[13] |
BARNICH O, VAN DROOGENBROECK M.ViBe:A universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing, 2011, 20(6):1709-1724. http://d.old.wanfangdata.com.cn/Periodical/yqyb201404029
|
[14] |
ROSTEN E, PORTER R, DRUMMOND T.Faster and better:A machine learning approach to corner detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1):105-119. http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0810.2434
|
[15] |
ILG E, MAYER N, SAIKIA T, et al.FlowNet 2.0: Evolution of optical flow estimation with deep networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway, NJ: IEEE Press, 2017: 1647-1655. https://www.researchgate.net/publication/311459265_FlowNet_20_Evolution_of_Optical_Flow_Estimation_with_Deep_Networks
|
[16] |
OCHS P, MALIK J, BROX T.Segmentation of moving objects by long term video analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(6):1187-1200. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ba6d9aaf6e0c534979b63876cd88075e
|
[17] |
TOLDO R, FUSIELLO A.Robust multiple structures estimation with J-linkage[C]//Proceedings of European Conference on Computer Vision.Berlin: Springer, 2008: 537-547.
|
[18] |
REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once: Unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway, NJ: IEEE Press, 2016: 779-788. https://www.researchgate.net/publication/278049038_You_Only_Look_Once_Unified_Real-Time_Object_Detection
|
[19] |
DANELLJAN M, BHAT G, KHAN F S, et al.ECO: Efficient convolution operators for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway, NJ: IEEE Press, 2017: 6638-6646. https://www.researchgate.net/publication/320971140_ECO_Efficient_Convolution_Operators_for_Tracking
|