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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
07.07.2022
DOI
https://doi.org/10.7307/ptt.v34i4.4041
ISSUE
2022 (Vol 34), Issue 4
LICENSE
Copyright (c) 2022 Quan CHEN, Hao WANG, Changyin DONG
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

Authors:Quan CHEN, Hao WANG, Changyin DONG

Abstract

This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily.

Keywords:video observation, integral trajectory extracting, vehicle tracking

References

  1. [1] Li M, Li Z, Xu C, Liu T. Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories. Accident Analysis & Prevention. 2020;135: 105345. doi: 10.1016/j.aap.2019.105345.
  2. [2] Xu C, Zhao J, Liu P. A geographically weighted regression approach to investigate the effects of traffic conditions and road characteristics on air pollutant emissions. Journal of Cleaner Production. 2019;239: 118084. doi: 10.1016/j.jclepro.2019.118084.
  3. [3] Wang H, Qin Y, Wang W, Chen J. Stability of CACC-manual heterogeneous vehicular flow with partial CACC performance degrading. Transportmetrica B: Transport Dynamics. 2019;7(1): 788-813. doi: 10.1080/21680566.2018.1517058.
  4. [4] Liu Z, Liu Y, Meng Q, Cheng Q. A tailored machine learning approach for urban transport network flow estimation. Transportation Research Part C: Emerging Technologies. 2019;108: 130-150. doi: 10.1016/j.trc.2019.09.006.
  5. [5] Wang C, Xu C, Dai Y. A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data. Accident Analysis & Prevention. 2019;123: 365-373. doi: 10.1016/j.aap.2018.12.013.
  6. [6] Zhao YC, et al. Driving rule extraction based on cognitive behavior analysis. Journal of Central South University. 2020;27(1): 164-179. doi: 10.1007/s11771-020-4286-1.
  7. [7] Sun C, et al. Multi-criteria user equilibrium model considering travel time, travel time reliability and distance. Transportation Research Part D: Transport and Environment. 2019;66: 3-12. doi: 10.1016/j.trd.2017.03.002.
  8. [8] Wang C, Sun Z, Ye Z. On-road bus emission comparison for diverse locations and fuel types in real-world operation conditions. Sustainability. 2020;12(5): 1798. doi: 10.3390/su12051798.
  9. [9] Gu X, et al. Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accident Analysis & Prevention. 2019;123: 159-169. doi: 10.1016/j.aap.2018.11.010.
  10. [10] Guo Y, Li Z, Liu P, Wu Y. Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model. Accident Analysis & Prevention. 2019;128: 164-174. doi: 10.1016/j.aap.2019.04.013.
  11. [11] Wang C, et al. A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation. Transportation Research Part C: Emerging Technologies. 2018;90: 281-291. doi: 10.1016/j.trc.2018.03.011.
  12. [12] Li XC, Fan LK, Chen T, Guo CS. Vehicle lane-changes trajectory prediction model considering external parameters. Promet – Traffic&Transportation. 2021;33(5): 745-754. doi: 10.7307/ptt.v33i5.3718.
  13. [13] Kim EJ, et al. Extracting vehicle trajectories using unmanned aerial vehicles in congested traffic conditions. Journal of Advanced Transportation. 2019; 9060797. doi: 10.1155/2019/9060797.
  14. [14] Eliker K, Zhang GQ, Grouni S, Zhang WD. An optimization problem for quadcopter reference flight trajectory generation. Journal of Advanced Transportation. 2018; 6574183. doi: 10.1155/2018/6574183.
  15. [15] Chen XQ, et al. High-resolution vehicle trajectory extraction and denoising from aerial videos. IEEE Transactions on Intelligent Transportation Systems. 2021;22(5): 3190-3202. doi: 10.1109/TITS.2020.3003782.
  16. [16] Feng RY, Fan CY, Li ZB, Chen XQ. Mixed road user trajectory extraction from moving aerial videos based on convolution neural network detection. IEEE Access. 2020;8: 43508-43519. doi: 10.1109/ACCESS.2020.2976890.
  17. [17] Lu SN, Song HS, Hua C, Wang GF. A point-based tracking algorithm for vehicle trajectories in complex environment. International Conference on Intelligent Systems Design and Engineering Applications. 2014. p. 69-73. doi: 10.1109/ISDEA.2014.24.
  18. [18] Bradski G. Computer vision face tracking for use in a perceptual user interface. Proceedings of IEEE Workshop on Applications of Computer Vision. 1998. p. 214-219.
  19. [19] Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. Proceedings of the International Joint Conference on Artificial Intelligence. 1981;2: 674-679.
  20. [20] Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. Proceedings of the British Machine Vision Conference. 2006;6: 1-10. doi: 10.5244/C.20.6.
  21. [21] Lukežič A, et al. Discriminative correlation filter tracker with channel and spatial reliability. International Journal of Computer Vision. 2018;126: 671-688. doi: 10.1007/s11263-017-1061-3.
  22. [22] Henriques JF, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. Proceedings of the European Conference on Computer Vision. 2012;4: 702-715. doi: 10.1007/978-3-642-33765-9_50.
  23. [23] Kalal Z, Mikolajczyk K, Matas J. Forward-backward error: Automatic detection of tracking failures. IEEE International Conference on Pattern Recognition. 2010. p. 2756-2759. doi: 10.1109/ICPR.2010.675.
  24. [24] Babenko B, Yang MH, Belongie S. Visual tracking with online multiple instance learning. IEEE Conference on Computer Vision and Pattern Recognition. 2009. p. 983-990. doi: 10.1109/CVPR.2009.5206737.
  25. [25] Bolme DS, Beveridge JR, Draper BA, Lui YM. Visual object tracking using adaptive correlation filters. IEEE Conference on Computer Vision and Pattern Recognition. 2010. p. 2544-2550. doi: 10.1109/CVPR.2010.5539960.
  26. [26] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34(7): 1409-1422. doi: 10.1109/TPAMI.2011.239.
  27. [27] Kaehler A, Bradski G. Learning OpenCV 3. O’Reilly Media, Inc. California, US; 2017. p. 644-671.
  28. [28] Semple J, Kneebone G. Algebraic projective geometry. Oxford, UK: Oxford University Press; 1979.
  29. [29] Harris C, Stephens M. A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference. 1988. p. 147-151.
  30. [30] Shi J, Tomasi C. Good features to track. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1994. p. 593-600. doi: 10.1109/CVPR.1994.323794.
  31. [31] Lucchese L, Mitra SK. Using saddle points for subpixel feature detection in camera calibration targets. Asia-Pacific Conference on Circuits and Systems. 2002;2: 191-195. doi: 10.1109/APCCAS.2002.1115151.
  32. [32] Chen D, Zhang G. A new sub-pixel detector for x-corners in camera calibration targets. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. 2005. p. 97-100.
  33. [33] Chen Q, Wang H, Dong CY. Modeling lane-changing behaviors in merging areas of urban expressways in Nanjing, China. Transportation Research Record. 2020;2674(7): 480-493. doi: 10.1177/0361198120923361.
  34. [34] Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the IEEE International Conference on Pattern Recognition. 2004;2: 28-31. doi: 10.1109/ICPR.2004.1333992.
  35. [35] Zivkovic Z, Heijden FV. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters. 2006:27(7): 773-780. doi: 10.1016/j.patrec.2005.11.005.
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