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On site processing of video stream for mapping traffic parameters

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Traffic surveillance provides crucial data for the operation of intelligent transportation systems. The growing number of cameras in the transport system poses a problem for the efficient processing of surveillance data. Processing of video data for extracting traffic parameters is usually done using image processing methods and requires substantial processing resources. An alternative way is to transform the video stream and map the traffic parameters using the obtained transform coefficients. Spatiotemporal wavelet transform of the video stream contents, using filter banks, is proposed for mapping traffic parameters. Performed tests prove good resilience to illumination changes of road scenes. Mapping errors are smaller than in the case of the commonly used video detectors at sites on multilane roads with low to moderate traffic load.
Rocznik
Tom
Strony
175--189
Opis fizyczny
Bibliogr. 30 poz.
Twórcy
  • Faculty of Transportand Aviation Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland
  • Faculty of Transportand Aviation Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland
Bibliografia
  • 1. Bok Jinjoo, Youngsang Kwon. 2016. „Comparable Measures of Accessibility to Public Transport Using the General Transit Feed Specification.” Sustainability 8(3). DOI: 10.3390/su8030224.
  • 2. Rith Monorom, Alexis Fillone, Jose Bienvenido M. Biona. 2019. „The Impact of Socioeconomic Characteristics and Land Use Patterns on Household Vehicle Ownership and Energy Consumption in an Urban Area with Insufficient Public Transport Service –A Case Study of Metro Manila.” Journal of Transport Geography 79: 102484. DOI: 10.1016/j.jtrangeo.2019.102484.
  • 3. Zhang Tingru, Alan H.S. Chan, Hongjun Xue, Xiaoyan Zhang, Da Tao. 2019. “Driving Anger, Aberrant Driving Behaviors, and Road Crash Risk: Testing of a Mediated Model.”International Journal of Environmental Research and Public Health 16(3): 1-13. DOI: 10.3390/ijerph16030297.
  • 4. Ortega Jairo, János Tóth, Tamás Péter. 2021. „Planning a Park and Ride System: A Literature Review.”Future Transportation 1(1): 82-98. DOI: 10.3390/futuretransp1010006.
  • 5. Federal Highway Administration. 2016. „Traffic Monitoring Guide FHWA.” Fhwa. Available at: http://www.fhwa.dot.gov/policyinformation/tmguide/.
  • 6. Klein Lawrence A. 2017. ITS Sensors and Architectures for Traffic Management and Connected Vehicles. Boca Raton : Taylor & Francis, CRC Press. DOI: 10.1201/9781315206905.
  • 7. Jiang Xiaomo, Hojjat Adeli. 2004. „Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis.” Computer-Aided Civil and Infrastructure Engineering 19(5): 324-37. DOI: 10.1111/j.1467-8667.2004.00360.x.
  • 8. Mandellos Nicholas A., Iphigenia Keramitsoglou, Chris T. Kiranoudis. 2011. „A Background Subtraction Algorithm for Detecting and Tracking Vehicles.” Expert Systems with Applications 38(3): 1619-31. DOI: 10.1016/j.eswa.2010.07.083.
  • 9. Tasgaonkar Pankaj P., Rahul Dev Garg, Pradeep Kumar Garg. 2020. „Vehicle Detection and Traffic Estimation with Sensors Technologies for Intelligent Transportation Systems.” Sensing and Imaging 21(1). DOI: 10.1007/s11220-020-00295-2.
  • 10. Singleton Patrick A., Keunhyun Park, Doo Hong Lee. 2021. „Varying Influences of the Built Environment on Daily and Hourly Pedestrian Crossing Volumes at Signalized Intersections Estimated from Traffic Signal Controller Event Data.” Journal of Transport Geography 93: 103067. DOI: 10.1016/j.jtrangeo.2021.103067.
  • 11. Buch Norbert, Sergio A. Velastin, James Orwell. 2011. „A Review of Computer Vision Techniques for the Analysis of Urban Traffic.” IEEE Transactions on Intelligent Transportation Systems 12(3): 920-39. DOI: 10.1109/TITS.2011.2119372.
  • 12. Semertzidis T., K. Dimitropoulos, A. Koutsia, N. Grammalidis. 2010. „Video Sensor Network for Real-Time Traffic Monitoring and Surveillance.” IET Intelligent Transport Systems 4(2): 103-12. DOI: 10.1049/iet-its.2008.0092.
  • 13. Roy Arunesh, Nicholas Gale, Lang Hong. 2011. „Automated Traffic Surveillance Using Fusion of Doppler Radar and Video Information.” Mathematical and Computer Modelling 54(1-2): 531-43. DOI: 10.1016/j.mcm.2011.02.043.
  • 14. Xu Yong, Jixiang Dong, Bob Zhang, Daoyun Xu. 2016. „Background Modeling Methods in Video Analysis: A Review and Comparative Evaluation.” CAAI Transactions on Intelligence Technology 1(1): 43-60. DOI: 10.1016/j.trit.2016.03.005.
  • 15. Garcia-Garcia Belmar, Thierry Bouwmans, Alberto Jorge Rosales Silva. 2020. „Background Subtraction in Real Applications: Challenges, Current Models and Future Directions.” Computer Science Review 35: 100204. DOI: 10.1016/j.cosrev.2019.100204.
  • 16. Mitrović Dejan. 2005. „Reliable Method for Driving Events Recognition.” IEEE Transactions on Intelligent Transportation Systems 6(2): 198-205. DOI: 10.1109/TITS.2005.848367.
  • 17. Lee Uichin, Mario Gerla. 2010. „A Survey of Urban Vehicular Sensing Platforms.” Computer Networks 54(4): 527-44. DOI: 10.1016/j.comnet.2009.07.011.
  • 18. Salih Yasir, Aamir Saeed Malik. 2011. „Comparison of Stochastic Filtering Methods for 3D Tracking.” Pattern Recognition 44(10-11): 2711-37. DOI: 10.1016/j.patcog.2011.03.027.
  • 19. Karasulu Bahadir, Serdar Korukoglu. 2012. „Moving Object Detection and Tracking by Using Annealed Background Subtraction Method in Videos: Performance Optimization.” Expert Systems with Applications 39(1): 33-43. DOI: 10.1016/j.eswa.2011.06.040.
  • 20. Guo Yulan, Mohammed Bennamoun, Ferdous Sohel, Min Lu, Jianwei Wan, Ngai Ming Kwok. 2016. „A Comprehensive Performance Evaluation of 3D Local Feature Descriptors.” International Journal of Computer Vision 116(1): 66-89. DOI: 10.1007/s11263-015-0824-y.
  • 21. Kihl Olivier, David Picard, Philippe-Henri Gosselin. 2015. „A Unified Framework for Local Visual Descriptors Evaluation.” Pattern Recognition 48(4): 1174-84. DOI: 10.1016/j.patcog.2014.11.013.
  • 22. Horn Berthold K.P., Brian G. Schunck. 1981. „Determining Optical Flow.” Artificial Intelligence 17(1-3): 185-203. DOI: 10.1016/0004-3702(81)90024-2.
  • 23. Peng Yanan, Zhenxue Chen, Q.M. Jonathan Wu, Chengyun Liu. 2018. „Traffic Flow Detection and Statistics via Improved Optical Flow and Connected Region Analysis.” Signal, Image and Video Processing 12(1): 99-105. DOI: 10.1007/s11760-017-1135-2.
  • 24. Adeli Hojjat, Samanwoy Ghosh-Dastidar. 2004. „Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Feature Extraction Model.” Journal of Transportation Engineering 130(1): 94-103. DOI: 10.1061/(ASCE)0733-947X(2004)130:1(94).
  • 25. Zheng Zuduo, Soyoung Ahn, Danjue Chen, Jorge Laval. 2011. „Applications of Wavelet Transform for Analysis of Freeway Traffic: Bottlenecks, Transient Traffic, and Traffic Oscillations.” Transportation Research Part B: Methodological 45(2): 372-84. DOI: 10.1016/j.trb.2010.08.002.
  • 26. Zheng Zuduo, Soyoung Ahn, Danjue Chen, and Jorge Laval. 2011. „Freeway Traffic Oscillations: Microscopic Analysis of Formations and Propagations Using Wavelet Transform.” Procedia - Social and Behavioral Sciences 17: 702-16. DOI: 10.1016/j.sbspro.2011.04.540.
  • 27. Ibtissam Slimani, Abdelmoghit Zaarane, Abdellatif Hamdoun, Issam Atouf. 2018. „Traffic Surveillance System For Vehicle Detection Using Discrete Wavelet Transfor.” Journal of Theoretical and Applied Information Technology 96(17). DOI: 10.13140/RG.2.2.34426.08649.
  • 28. Bendali-Braham Mounir, Jonathan Weber, Germain Forestier, Lhassane Idoumghar, Pierre-Alain Muller. 2021. „Recent Trends in Crowd Analysis: A Review.” Machine Learning with Applications 4: 100023. DOI: 10.1016/j.mlwa.2021.100023.
  • 29. US4847772A. Michalopoulos Panos G., A. Richard Fundakowski, Meletios Geokezas, Robert C. Fitch. 1989. "Vehicle detection through image processing for traffic surveillance and control". Patent number 4,847,772. Available at: https://patents.google.com/patent/US4847772A/en.
  • 30. Mallat Stephane G. 2009. „Multiresolution Approximations and Wavelet Orthonormal Bases of L2(R).” Fundamental Papers in Wavelet Theory 315(1): 524-42. DOI: 10.1090/s0002-9947-1989-1008470-5.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bedcf186-54f5-4b24-a2a8-a456036015ea
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