Abstract
The increase in daily traffic volume needs a more effective, intelligent, and sophisticated traffic management and control strategy. Video-based traffic monitoring, which incorporates computer vision algorithms, has become one of the most viable and widely utilised intelligent transportation systems (ITS)-based solutions in recent years. The two primary objectives in existing video-based traffic monitoring are to achieve higher accuracy in detecting vehicle and reducing the processing cost. In order to achieve these objectives, a recurrent architecture for parallel vehicle detection scheme (RAP-VDS) with the following two modules: multilevel parallel spatial color information processing (MSCIP) and reduction of redundant temporal color information (R2TCI) are proposed in this work. The MSCIP module uses spatial colour information to increase detection accuracy, whereas R2TCI reduces processing time by eliminating repeated frames over time. The vehicles detected using specified virtual segment within the video frames using RAP-VDS is presented. The results show that irrespective of the computer vision methods used for vehicle detection, by incorporating RAP-VDS there is an improvement in accuracy and processing time.
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Yang Z, Pun-Cheng LSC. Vehicle detection in intelligent transportation systems and its applications under varying environments: a review. J Image Vis Comput. 2018;69:143–54.
Manipriya S, Mala C, Samson M. Virtual mono-layered continuous containers for vehicle detection applications in intelligent transportation systems. J Discret Math Sci Cryptogr (JDMSC). 2020;23(1):321–8.
Sankaranarayanan M, Mala C, Mathew S. Significance of real time systems in intelligent transportation systems. In: Handling priority inversion in time-constrained distributed databases, 2020; p. 61–85.
Loce RP, Bala R, Trivedi M. Computer vision and imaging in intelligent transportation system. 1st ed. Hoboken: Wiley Publications; 2017.
Kastrinaki V, Zervakis M, Kalaitzakis K. A survey of video processing techniques for traffic applications. J Image Vis Comput. 2016;21:359–81.
Yaghoobi Ershadi N, Menéndez JM, Jiménez D. Robust vehicle detection in different weather conditions: using MIPM. PLoS ONE. 2018;13(3):e0191355.
Shah M, Deng JD, Woodford BJ. Video background modeling: recent approaches, issues and our proposed techniques. J Mach Vis Appl. 2014;25:1105–19.
Brutzer S, Höferlin B, Heidemann G. Evaluation of background subtraction techniques for video surveillance. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011.
Bouwmans T, Garcia Garcia B. Background subtraction in real applications: challenges, current models and future directions. 2019; eprint 1901.03577.
Huo Z, Xia Y, Zhang B. Vehicle type classification and attribute prediction using multi-task RCNN. In: 2016 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), Datong. 2016, p. 564–569.
Piccardi M. Background subtraction techniques: a review. In: IEEE international conference on systems, man and cybernetics, vol 4, p. 3099–3104.
Mohameda A, Issama A, Mohameda B, Abdellatifa B. Real-time detection of vehicles using the Haar-like features and artificial neuron networks. J Procedia Comput Sci Adv Wirel Inf Commun Technol. 2015;73:24–31.
Yang S, Xu J, Wang MH. Onboard vehicle detection and tracking using boosted Gabor descriptor and sparse representation. IEEE Electron Lett. 2012;48(16):995–7.
Stauffer C, Grimson WEL. Adaptive background mixture models for real-time tracking. In: Proc. of CVPR. 1999, p. 246–252
Zhang Y, Zhao C, He J, Chen A. Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement. IEEE J IET Intell Transp Syst. 2016;10(6):445–52.
Kanagamalliga S, Vasuki S, Shanmugapriya M. Foreground object detection using expectation maximization based effective Gaussian mixture model. Middle-East J Sci Res. 2016;24(Special Issue on Innovations in Information, Embedded and Communication Systems):51–7.
Chen Z, Ellis T. A self-adaptive Gaussian mixture model. SCI J Comput Vis Image Underst. 2014;122:35–46.
Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. Int Conf Pattern Recognit. 2004;2:28–31.
KadewTraKuPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of 2nd European workshop on advanced video-based surveillance systems, 2002. p. 135–144.
El Baf F, Bouwmans T, Vachon B. Fuzzy integral for moving object detection. In: IEEE international conference on fuzzy systems, 2008. p. 1729–1736.
Reddy SK, Ram B, O’Byrne M, Vanajakshi L, Ghosh B. Alternative approach to traffic state analysis on indian roads using image processing. In: Proceedings of the institution of civil engineers-transport, 2018. p. 1–11.
Manana M, Tu C, Owolawi PA. A survey on vehicle detection based on convolution neural networks. In: 3rd IEEE international conference on computer and communications, 2017. p. 1751–1755.
Donato I. Vehicular traffic congestion classification by visual features and deep learning approaches: a comparison. Sensors (Basel, Switzerland). 2019;19(23):5213.
Kim K, Chalidabhongse TH, Harwood D, Davis L. Real-time foreground-background segmentation using codebook model. J Real Time Imaging. 2005;11:172–85.
Barnich O, Van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process. 2011;20(6):1709–24.
Wen L, Du D, Cai Z, Lei Z, Chang M-C, Qi Ho, Lim J, Yang M-H, Lyu S. UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking. J Comput Vis Image Underst 2020; 193
Background Subtraction using OpenC (2019) https://docs.opencv.org/3.4/d1/dc5/tutorial_background_subtraction.html.
Manipriya S, Mala C, Mathew S. Performance analysis of spatial color information for object detection using background subtraction. IERI Procedia. 2014;10:63–9.
Manipriya S, Ramadurai G, Bhavesh Reddy VV. Grid-based real time image processing (GRIP) algorithm for heterogeneous traffic. In: IEEE international conference on communication systems and networks, IEEE Publications; 2015. p. 1–6
Monika. Parallel processing techniques for high performance image processing applications. In: International conference on electrical, electronics and computer science (SCEECS), IEEE Publications; 2016.
Manipriya S, Mala C, Samson M. Performance analysis of spatial color information for object detection using background subtraction. In: International conference on future information engineering (FIE), Published in Elsevier IERI Procedia, Scopus Indexed, 2014; vol. 10, p. 63-69
Gonzalez Rafael C, Woods Richard E. Digital image processing. 4th ed. Upper Saddle River: Pearson Publications; 2017.
Otsu N. A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern. 1979;9:62–6.
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Sankaranarayanan, M., Mala, C. & Mathew, S. Improved Vehicle Detection Accuracy and Processing Time for Video Based ITS Applications. SN COMPUT. SCI. 3, 251 (2022). https://doi.org/10.1007/s42979-022-01130-z
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DOI: https://doi.org/10.1007/s42979-022-01130-z