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Fast approach for moving vehicle localization and bounding box estimation in highway traffic videos

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Abstract

Detection and accurate localization of position of moving vehicles in a video sequence are the primary tasks in every computer vision-based traffic monitoring system. In this paper, a moving vehicle localization and bounding box estimation algorithm is proposed. The moving vehicle foreground is separated from the static background by the adaptive background subtraction method, and the bounding box of moving vehicles is estimated with two-dimensional binary histogram projection profile (2D-BHPP) algorithm. The foreground object refinement is performed by means of morphological closing operation prior to apply the 2D-BHPP algorithm. The proposed method only computes the four necessary minimum bounding box coordinates, i.e., left, right, upper, and lower of moving vehicles in every frame. The proposed method is tested over three publicly available data sets, and the results show that the localization algorithm works comparatively faster and accurately than the existing localization methods. The detection error rate, IoU metric, and execution time per frame signify that this approach can be implemented in edge-based real-time applications.

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References

  1. Alessandretti, G., Broggi, A., Cerri, P.: Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans. Intell. Trans. Syst. 8(1), 95–105 (2007)

    Article  Google Scholar 

  2. Jo, Y., Jung, I.: Analysis of vehicle detection with wsn-based ultrasonic sensors. Sensors 14, 4050–14069 (2014)

    Article  Google Scholar 

  3. Mimbela, L.E.Y., Klein, L.A.: Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems. Technical Report, Federal Highway Administration s (FHWA) Intelligent Transportation Systems Joint Program Office (2000)

  4. Wang, G., Xiao, D., Gu, J.: Review on vehicle detection based on video for traffic surveillance. In: 2008 IEEE International Conference on Automation and Logistics, pp. 2961–2966 (2008)

  5. Sengar, S.S., Mukhopadhyay, S.: A novel method for moving object detection based on block based frame differencing. In: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), pp. 467–472 (2016)

  6. Dou, J., Qin, Q., Tu, Z.: Background subtraction based on circulant matrix. Signal, Image Video Process. 11(3), 407–414 (2017)

    Article  Google Scholar 

  7. Singh Sengar, S., Mukhopadhyay, S.: Moving object area detection using normalized self adaptive optical flow. Optik - Int. J. Light Electron. Optics. 127, 6258–6267 (2016)

    Article  Google Scholar 

  8. Suzuki, S.: Abe K, : Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Processing 30, 32–46 (1985)

    Article  Google Scholar 

  9. Yanan Peng, Q.M.J.W., Chen, Z., Liu, C.: Traffic flow detection and statistics via improved optical flow and connected region analysis. Signal, Image Video Process. 12(99), 105 (2018)

    Google Scholar 

  10. Jaekyu, H.a., Haralick, R.M., Phillips, I.T.: Document page decomposition by the bounding-box project. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol 2, pp. 1119–1122 (1995)

  11. Li, G., Song, H., Wang, S., Kong, J.: Application of image processing and three-dimensional data reconstruction algorithm based on traffic video in vehicle component detection. Math. Problems Eng. 2017, 1–16 (2017)

    Google Scholar 

  12. Weng, M., Huang, G., Da, X.: A new interframe difference algorithm for moving target detection. In: 2010 3rd International Congress on Image and Signal Processing, vol 1, pp. 285–289 (2010)

  13. Ramesh, S., Upadhyaya, V.: Vehicle classification and lane categorization. In: 2016 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 17–22 (2016)

  14. Collins, R,. Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O.: A system for video surveillance and monitoring. Technical Report CMU-RI-TR-00-12, Carnegie Mellon University, Pittsburgh, PA (2000)

  15. Sengar, S.S., Mukhopadhyay, S.: Moving object detection based on frame difference and w4. Signal, Image Video Process. 11(7), 1357–1364 (2017)

    Article  Google Scholar 

  16. Honghong Yang, S.Q.: Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition. IET Intell. Trans. Syst. 12, 75–85 (2018)

    Article  Google Scholar 

  17. Bouwmans, T.: Recent advanced statistical background modeling for foreground detection: A systematic survey. Recent Patents Comput Sci 4, 147–176 (2011)

    Google Scholar 

  18. Shirazi, M.S., Morris, B.T.: Vision based turning movement monitoring: Count, speed and waiting time estimation. IEEE Intell. Trans. Syst. Magaz. 8, 23–34 (2016)

    Article  Google Scholar 

  19. Putra, B.C., Setiyono, B., Sulistyaningrum, D.R., Soetrisno, Mukhlash, I.: Moving vehicle classification using pixel quantity based on gaussian mixture models. In: 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, pp. 254–257 (2018). https://doi.org/10.1109/CCOMS.2018.8463218

  20. Bouwmans, T., Baf, F., Vachon, B.: Statistical Background Modeling for Foreground Detection: A Survey 4, 181–199 (2010)

  21. Akhan Almagambetov, S.V., Casares, M.: Robust and computationally lightweight autonomous tracking of vehicle taillights and signal detection by embedded smart cameras. IEEE Trans. Ind. Elect. 62, 3732–3741 (2015)

    Article  Google Scholar 

  22. Arrspide, J., Salgado, L.: A study of feature combination for vehicle detection based on image processing. Sci. World J. 1, 1–13 (2014)

    Article  Google Scholar 

  23. Deepambika, V.A., Rahman, M.A.: Illumination invariant motion detection and tracking using smdwt and a dense disparity-variance method. J. Sensors 1, 1–13 (2018)

    Article  Google Scholar 

  24. Ahmad Arinaldi, J.A.P., Gurusinga, A.A.: Moving object detection based on frame difference and w4. Procedia Comput. Sci. 144, 259–268 (2018)

    Article  Google Scholar 

  25. Lili Chen, Z.Z., Peng, L.: Fast single shot multibox detector and its application on vehicle counting system. IET Intell. Trans. Syst. 12, 1406–1413 (2018)

    Article  Google Scholar 

  26. Xu, H., Zhou, W., Zhu, J., Huang, X., Wang, W.: Vehicle counting based on double virtual lines. Signal, Image Video Process. 11(5), 905–912 (2017)

    Article  Google Scholar 

  27. Shiva Kamkar, R.S.: Vehicle detection, counting and classification in various conditions. IET Intell. Trans. Syst. 10, 406–413 (2016)

    Article  Google Scholar 

  28. Zhan, W., Ji, X.: Algorithm research on moving vehicles detection. Proc. Eng. 15, 5483–5487 (2011)

    Article  Google Scholar 

  29. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)

    Article  Google Scholar 

  30. Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J Graphics Tools 12, 13–21 (2007)

    Article  Google Scholar 

  31. Guerrero-Gómez-Olmedo, R., López-Sastre, R.J., Maldonado-Bascón, S., Fernández-Caballero, A.: Vehicle tracking by simultaneous detection and viewpoint estimation. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz, L.F., Toledo Moreo, F.J. (eds.) Natural and Artificial Computation in Engineering and Medical Applications, pp. 306–316. Springer, Berlin Heidelberg, Berlin, Heidelberg (2013)

    Chapter  Google Scholar 

  32. Zapletal, D., Herout, A.: Vehicle re-identification for automatic video traffic surveillance. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1568–1574 (2016)

  33. Wicaksono, D.W., Setiyono, B.: Speed estimation on moving vehicle based on digital image processing. Int. J. Comput. Sci. Appl. Math. 3, 21–26 (2017)

    Article  Google Scholar 

  34. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 658–666 (2019)

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Correspondence to Harikrishnan P M.

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This work was funded by Vandi Technologies PTE LTD Singapore, (Grant No. VANDI/PS01/NITT1821 dated 10-09-2018).

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M, H.P., Thomas, A., Gopi, V.P. et al. Fast approach for moving vehicle localization and bounding box estimation in highway traffic videos. SIViP 15, 1041–1048 (2021). https://doi.org/10.1007/s11760-020-01829-7

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  • DOI: https://doi.org/10.1007/s11760-020-01829-7

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