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Vision-based surveillance system for monitoring traffic conditions

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Abstract

With the rapid advancement of sensing technologies, it has been feasible to collect various types of traffic data such as traffic volume and travel times. Vision-based approach is one of the major scheme actively used for the automated traffic data collection, and continues to gain traction to a broader utilization. It collects video streams from cameras installed near roads, and processes the video streams frame by frame using image processing algorithms. The widely used algorithms include vehicle detection and vehicle tracking which recognize every vehicle in the camera view and track it in the consecutive frames. Vehicle counts and speed can be estimated from the detection and tracking results. Continuous efforts have been made for the performance improvement of the algorithms and for their effective applications. However, little research has been found on the application to the various view settings of highway CCTV cameras as well as the reliability of the speed estimation. This paper proposes a vision-based system that integrates vehicle detection, vehicle tracking, and field of view calibration algorithms to obtain vehicle counting data and to estimate individual vehicle speed. The proposed system is customized for the video streams collected from highway CCTVs which have various settings in terms of focus and view angles. The system detects and tracks every vehicle in the view unless it is occluded by other vehicles. It is also capable of handling occlusions that occurs frequently depending on the view angles. The system has been tested on the several different views including congested scenes. Vehicle counts and speed estimation results are compared to the manual counting and GPS data, respectively. The comparison signifies that the system has a high potential to extract reliable information about highway traffic conditions from highway CCTVs.

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References

  1. Bas E, Tekalp M, Salman FS (2007) Automatic vehicle counting from video for traffic flow analysis. 2007 I.E. Intell Vehicles Symp 1-3:1085–1090

    Google Scholar 

  2. Bouttefroy PLM, Bouzerdoum A, Phung SL, Beghdadi A (2008) Vehicle tracking by non-drifting mean-shift using projective Kalman Filter. Proc 11th Int IEEE Conf Intell Transportat Syst: 61–66

  3. Chaiyawatana N, Uyyanonvara B, Kondo T, Dubey P, Hatori Y (2011) Robust object detection on video surveillance. Proc 8th Int Joint Conf Comput Sci Softw Eng: 149–153

  4. Ervin R, MacAdam C, Walker J, Bogard S, Hagan M, Vayda A, Anderson E (2000) System for assessment of the vehicle motion environment (SAVME). University of Michigan Transportation Research Institute, Ann Arbor

    Google Scholar 

  5. Feris R, Petterson J, Siddiquie B, Brown L, Pankanti S (2011) Large-scale vehicle detection in challenging urban surveillance environments. IEEE Workshop Appl Comput Vision (WACV) 2011:527–533

    Google Scholar 

  6. FLIR Monitoring traffic at the Rion-Antirion Bridge, Day and Night. <http://www.flir.co.uk/traffic/display/?id=63198 > (Accessed at January 7, 2017)

  7. Gokule R, Kulkarni A (2014) Video based vehicle speed estimation and stationary vehicle detection. Int J Adv Foundation Res Comput (IJAFRC) 1(11):93–99

    Google Scholar 

  8. Harsha SS, Anne KR (2016) A highly robust vehicle detection, tracking and speed measurement model for intelligent transport systems. Int J Appl Eng Res 11(5):3733–3742

    Google Scholar 

  9. Hsieh JW, Yu SH, Chen YS, Hu WF (2006) Automatic traffic surveillance system for vehicle tracking and classification. IEEE T Intell Transp 7(2):175–187

    Article  MATH  Google Scholar 

  10. Huang L (2010) Real-time multi-vehicle detection and sub-feature based tracking for traffic surveillance systems. 2010 2nd Int Asia Conf Inform Contrl, Automation Robot (CAR) 2:324–328

    Article  Google Scholar 

  11. Kanheer NK (2008) Vision-based detection, tracking and classification of vehicles using stable features with automatic camera calibration. Clemson University

  12. Kanhere NK, Birchfield ST, Sarasua WA, Whitney TC (2007) Real-time detection and tracking of vehicle base fronts for measuring traffic counts and speeds on highways. Transport Res Rec 1993:155–164

    Article  Google Scholar 

  13. Kanhere NK, Birchfield ST (2008) Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features. IEEE T Intell Transp 9(1):148–160

    Article  Google Scholar 

  14. Kanhere NK, Birchfield ST (2010) A taxonomy and analysis of camera calibration methods for traffic monitoring applications. IEEE T Intell Transp 11(2):441–452

    Article  Google Scholar 

  15. Kim Z, Cao M (2010) Evaluation of feature-based vehicle trajectory extraction algorithm. 13th Int IEEE Ann Conf Intell Transport Syst, Proc, Madeira Island, Portugal: 99–104

  16. Kim Z, Gomes G, Hranac R, Skabardonis A (2005) A machine vision system for generating vehicle trajectories over extended freeway segments. Proc 12th World Congress Intell Transport Syst

  17. Kim Z, Malik J (2003) Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. Ninth IEEE Int Conf ComputVision, Vols I and Ii, Proceedings, 524-531

  18. Lienhart R, Maydt J (2002) An extended set of haar-like features for rapid object detection. Proc 2002 Int Conf Imag Process 1:900–903

    Article  Google Scholar 

  19. Liu T, Zheng NN, Zhao L, Cheng H (2005) Learning based symmetric features selection for vehicle detection. IEEE Intell Vehicles Symp Proc 2005:124–129

    Google Scholar 

  20. Malinovskiy Y, Wu YJ, Wang YH (2009) Video-based vehicle detection and tracking using spatiotemporal maps. Transport Res Rec (2121), 81–89

  21. Mcfarlane NJB, Schofield CP (1995) Segmentation and tracking of piglets in images. Mach Vis Appl 8(3):187–193

    Article  Google Scholar 

  22. Melo J, Naftel A, Bernardino A, Santos-Victor J (2006) Detection and classification of highway lanes using vehicle motion trajectories. IEEE T Intell Transp 7(2):188–200

    Article  Google Scholar 

  23. Miovision (2016) Miovision Traffic Data. <https://miovision.com/traffic-data/ > (Accessed at January 7, 2017)

  24. Mu K, Hui F, Zhao X (2016) Multiple vehicle detection and tracking in highway traffic surveillance video based on SIFT feature matching. J Inf Process Syst 12(2):183–195

    Google Scholar 

  25. NGSIM (2006) Next generation simulation <http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm> (Accessed at June 4, 2012)

  26. Robert K (2009) Video-based traffic monitoring at day and night Vehicle Features Detection and Tracking. 2009 12th Int IEEE Conf Intell Transport Syst (Itsc 2009), 285–290

  27. Rodriguez T, Garcia N (2010) An adaptive, real-time, traffic monitoring system. Mach Vision Appl 21(4):555–576

    Article  Google Scholar 

  28. Ross D, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141

    Article  Google Scholar 

  29. Scharcanski J, de Oliveira AB, Cavalcanti PG, Yari Y (2011) A particle-filtering approach for vehicular tracking adaptive to occlusions. IEEE T Veh Technol 60(2):381–389

    Article  Google Scholar 

  30. Skimson E (2016) The evolution of data-driven traffic operations. <https://miovision.com/blog/evolution-data-driven-traffic-operations> (Accessed at January 7, 2017)

  31. Tamersoy B, Aggarwal JK (2009) Robust Vehicle detection for tracking in highway surveillance videos using unsupervised learning. Avss: 2009 6th IEEE Int Conf Adv Video Signal Based Surv: 529–534

  32. TrafficVision “Video Analytics Detection for KC SCOUT.” TrafficVision™ Case Study < http://www.trafficvision.com/kc-scout-case-study> (Accessed at January 7, 2017)

  33. Tsai LW, Chean YC, Ho CP, Gu HZ, Lee SY (2011) Multi-lane detection and road traffic congestion classification for intelligent transportation system. Energy Procedia 13:3174–3182

    Article  Google Scholar 

  34. Tsuchiya M, Fujiyoshi H (2006) Evaluating feature importance for object classification in visual surveillance. Int C Patt Recog: 978–981

  35. Uzkent B, Hoffman MJ, Vodacek A (2016) Real-time vehicle tracking in aerial video using hyperspectral features. IEEE Conf Comput Vision Pattern Recogn Workshops 2016:1443–1451

    Google Scholar 

  36. Vargas M, Milla JM, Toral SL, Barrero F (2010) An enhanced background estimation algorithm for vehicle detection in urban traffic scenes. IEEE T Veh Technol 59(8):3694–3709

    Article  Google Scholar 

  37. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proc IEEE Comput Soc Conf Comput Vision Pattern Recogn 1:511–518

    Google Scholar 

  38. Xiong C-Z, Pang Y-G, Li Z-X, Liu Y-L, Li Y-H (2009) Vehicle tracking from videos based on mean shift algorithm. Proc ICCTP 2009: Crit Issues Transport Syst Plan, Dev, Manag: 486–493

  39. Yabo A, Arroyo S, Safar F, Oliva D (2016) Vehicle classification and speed estimation using computer vision techniques. Proc AADECA 2016 – Semana del Control Automatico, Buenos Aires, Argentina

  40. Zhang L, Li SZ, Yuan XT, Xiang SM (2007) Real-time object classification in video surveillance based on appearance learning. Proc Cvpr IEEE: 3766–3773

  41. Zhang ZX, Li M, Huang KQ, Tan TN (2008) Boosting local feature descriptors for automatic objects classification in traffic scene surveillance. 19th Int Conf Pattern Recogn 1–6:3918–3921

    Google Scholar 

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Acknowledgements

This work was supported by NRF-2014R1A1A2054793 and Transportation & Logistics Research Program ID-97344.

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Authors and Affiliations

  1. Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggi-do, Korea, 17058

    Man-Woo Park

  2. Stanford University, 473 Via Ortega, Stanford, CA, 94305, USA

    Jung In Kim

  3. Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, Korea, 44919

    Young-Joo Lee

  4. Hanyang University, 55 Hanyangdaehak-Ro, Sangnok-Gu, Ansan, Korea, 15588

    Jinwoo Park & Wonho Suh

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  1. Man-Woo Park
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  2. Jung In Kim
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  4. Jinwoo Park
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  5. Wonho Suh
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Correspondence to Wonho Suh.

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Park, MW., In Kim, J., Lee, YJ. et al. Vision-based surveillance system for monitoring traffic conditions. Multimed Tools Appl 76, 25343–25367 (2017). https://doi.org/10.1007/s11042-017-4521-4

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  • DOI: https://doi.org/10.1007/s11042-017-4521-4

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