Abstract
Traffic analysis using computer vision methods becoming an important field in the traffic analysis research area. Despite this, common traffic models still rely on traffic planning methods, which treat all cars uniformly, meanwhile special vehicle can bypass traffic rules. Considering this, special vehicle can travel noticeably faster by avoiding traffic jams. This paper presents an analysis of special transport behavior case - movement by the opposite lane(MBOL). Our goal is to analyze under which conditions, such kind of specific traffic behavior happens and to present a regression model, which further can be used in special transport route planning systems or transport model simulations. The video from the traffic surveillance camera on the Nevsky Prospect (the central street of the city of Saint-Petersburg) have been used. To analyze traffic conditions (and detect MBOL-cases) we use well-established computer vision methods - Viola-Jones for vehicle detection and MedianFlow/KCF for vehicle tracking. Results show that MBOL happens under extreme main lane conditions (high traffic density and flow), with wide variety of parameters for the opposite lane.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
López, B., Innocenti, B., Aciar, S., Cuevas, I.: A multi-agent system to support ambulance coordination in time-critical patient treatment. In: 7th Simposio Argentino de Intelligencia Artificial-ASAI2005 (2005)
Vlad, R.C., Morel, C., Morel, J.-Y., Vlad, S.: A learning real-time routing system for emergency vehicles. In: 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2008, pp. 390–395 (2008)
Gayathri, N., Chandrakala, K.R.M.V.: A novel technique for optimal vehicle routing. In: 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1–5 (2014)
Ibri, S., Nourelfath, M., Drias, H.: A multi-agent approach for integrated emergency vehicle dispatching and covering problem. Eng. Appl. Artif. Intell. 25, 554–565 (2012). https://doi.org/10.1016/j.engappai.2011.10.003
Créput, J.-C., Hajjam, A., Koukam, A., Kuhn, O.: Dynamic vehicle routing problem for medical emergency management. In: Self Organizing Maps-Applications and Novel Algorithm Design. InTech (2011)
Maxwell, M.S., Restrepo, M., Henderson, S.G., Topaloglu, H.: Approximate dynamic programming for ambulance redeployment. INFORMS J. Comput. 22, 266–281 (2010). https://doi.org/10.1287/ijoc.1090.0345
Haas, O.C.: Ambulance response modeling using a modified speed-density exponential model. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 943–948 (2011)
Liu, X., Nie, M., Jiang, S., et al.: Automatic traffic abnormality detection in traffic scenes: an overview. DEStech Trans. Eng. Technol. Res. (2017)
Derevitskii, I., Kurilkin, A., Bochenina, K.: Use of video data for analysis of special transport movement. Procedia Comput. Sci. 119, 262–268 (2017). https://doi.org/10.1016/J.PROCS.2017.11.184
Zhang, J.-D., Xu, J., Liao, S.S.: Aggregating and sampling methods for processing GPS data streams for traffic state estimation. IEEE Trans. Intell. Transp. Syst. 14, 1629–1641 (2013)
Wing, M.G., Eklund, A., Kellogg, L.D.: Consumer-grade Global Positioning System (GPS) accuracy and reliability. J For 103, 169–173 (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, p. I (2001)
Cheung, S.-C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Proceedings of SPIE, pp. 881–892 (2004)
McConnell, R.K.: Method of and apparatus for pattern recognition (1986)
Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12, 920–939 (2011)
Chen, X., Zhang, C., Chen, S.-C., Rubin, S.: A human-centered multiple instance learning framework for semantic video retrieval. IEEE Trans. Syst. Man Cybern. Part C Applications Rev 39, 228–233 (2009)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Patt. Anal. Mach. Intell. 37, 583–596 (2015)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)
Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2756–2759 (2010)
Nellore, K., Hancke, G.P.: Traffic management for emergency vehicle priority based on visual sensing. Sensors 16, 1892 (2016)
Saint Petersburg TV Channel (2018) Nevsky Prospect Street Camera. https://topspb.ru/online-projects/46
Grigorev, A.: Nevsky prospect traffic surveillance video (2018). https://doi.org/10.6084/m9.figshare.5841846.v5
Grigorev, A.: Nevsky prospect traffic surveillance video(movement by the oncoming lane cases hours) (2018). https://doi.org/10.6084/m9.figshare.5841267.v3
Li, X., She, Y., Luo, D., Yu, Z.: A traffic state detection tool for freeway video surveillance system. Procedia – Soc. Behav. Sci. 96, 2453–2461 (2013). https://doi.org/10.1016/j.sbspro.2013.08.274
Acknowledgements
This research is financially supported by The Russian Science Foundation, Agreement №17-71-30029 with co-financing of Bank Saint Petersburg.
We thank our colleagues from ITMO University for their support and encouragement which gave us an inspiration to complete our research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Artur, G., Derevitskii, I., Bochenina, K. (2018). Analysis of Special Transport Behavior Using Computer Vision Analysis of Video from Traffic Cameras. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2018. Communications in Computer and Information Science, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-02843-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-02843-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02842-8
Online ISBN: 978-3-030-02843-5
eBook Packages: Computer ScienceComputer Science (R0)