Abstract
Limited studies are conducted to develop trajectory-level data extraction tools for non-lane-based heterogeneous traffic conditions prevalent in developing countries, like India. Comprehending this research gap, the development of an automation-based conceptual methodology is crucial, that would help researchers to understand and assess heterogeneous, non-lane-based traffic at micro-level. Microscopic parameters like individual vehicular trajectory and inter-vehicular spacing, in lateral as well as longitudinal dimensions, play significant roles in understanding traffic dynamics. A major hindrance in acquiring the huge data required for this purpose is the high demand of manpower and time in extracting traffic data manually with desirable accuracy. Implementing automated systems can mitigate the burden of data acquisition. One of the promising ideas for effective traffic analysis is video-based image processing, using which highly accurate data can be obtained by suitably calibrating the threshold values, thereby optimizing it for a given video. In this study, an attempt has been made to develop an automated image processing tool using MATLAB, to first classify vehicles under varying roadway and traffic conditions, and then to obtain lateral as well as longitudinal spacing maintained, based on the detected positions of vehicles on the road over time. The developed traffic data extractor also provides output on individual vehicle trajectory, and, hence, travel times and speed profiles of different vehicle categories. Evaluation results showed an MAPE of less than 13%, suggesting its reliability under varying mixed traffic conditions. The logic developed in this research is expected to cater well for similar traffic conditions in other Asian countries.
Similar content being viewed by others
References
Keith RJ, Tindall JI, Yan ST (1964) The performance and characteristics of a magnetic loop vehicle detector. In: 2nd Australian Road Research Board (ARRB) Conference, Melbourne, vol. 2, no. 1
MacCarley CA, Hockaday SL, Need D, Taff SS (1992) Evaluation of video image processing systems for traffic detection (Abridgment). Transportation Research Record (1360)
Mallikarjuna C, Phanindra A, Rao KR (2009) Traffic data collection under mixed traffic conditions using video image processing. J Transp Eng 135(4):174–182
Bharadwaj N, Kumar P, Arkatkar S, Maurya A, Joshi G (2016) Traffic data analysis using image processing technique on Delhi-Gurgaon expressway. Curr Sci 110(5):00113891
MATLAB (2016) The MathWorks Inc., Natick, MA
Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: Pattern Recognition. ICPR 2004. IEEE
Yoneyama A, Yeh CH, Kuo CC (2003) Moving cast shadow elimination for robust vehicle extraction based on 2D joint vehicle/shadow models. In: IEEE conference on advanced video and signal based surveillance. IEEE, pp 229–236
Zhan C, Duan X, Xu S, Song Z, Luo M (2007) An improved moving object detection algorithm based on frame difference and edge detection. In: Image and graphics, ICIG 2007. IEEE
Gao W, Zhang X, Yang L, Liu H (2010) An improved Sobel edge detection. In: 3rd IEEE international conference on computer science and information technology (ICCSIT), vol 5. IEEE, pp 67–71
Papageorgiou CP, Oren M, Poggio T (1998) A general framework for object detection. In: Sixth international conference on computer vision. IEEE, pp 555–562
Kim Z, Malik J (2003) Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. In: Transactions. IEEE, p 524
Coifman B, Beymer D, McLauchlan P, Malik J (1998) A real-time computer vision system for vehicle tracking and traffic surveillance. Transp Res C Emerg Technol 6(4):271–288
Singh S, Prasad A, Srivastava K, Bhattacharya S (2016) A cellular logic array-based data mining framework for object detection in video surveillance system. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)—Proceeding. IEEE, pp 719–724
Muad AM, Hussain A, Samad SA, Mustaffa MM, Majlis BY (2004) November. Implementation of inverse perspective mapping algorithm for the development of an automatic lane tracking system. In: TENCON. IEEE
Acknowledgements
The authors are grateful to the Center of Excellence in Urban Transport at IIT Madras for providing the video graphic data of I.T. Corridor, Chennai. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The authors acknowledge the opportunity provided by the 4th Conference of the Transportation Research Group of India (4th CTRG) held at IIT Bombay, Mumbai, India from 17th to 20th December, 2017 to present the work that forms the basis of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Raveendran, B., Arkatkar, S.S. & Vanajakshi, L.D. Development of a Video Image Processing-Based Micro-level Data Extractor for Non-lane-Based Heterogeneous Traffic Conditions. Transp. in Dev. Econ. 5, 12 (2019). https://doi.org/10.1007/s40890-019-0084-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40890-019-0084-6