Abstract
Vehicle detection and classification is an essential application in traffic surveillance system (TSS). Recent studies have solely focused on vehicle detection in the daytime scenes. However, recognizing moving vehicle at nighttime is more challenging because of either poor (lack of street lights) or bright illuminations (vehicle headlight reflection on the road). These problems hinder the ability to identify vehicle’s shapes, sizes, or textures which are mainly used in daytime surveillance. Hence, vehicles’ headlights are the only visible features. However, the tracking and pairing of vehicle’s headlights have its own challenge because of chaotic traffic of motorbikes. Adding to this is various types of vehicles travel on the same road which falsifies the pairing results. So, this research proposes an algorithm for vehicle detection and classification at nighttime surveillance scenes which consists of headlight segmentation, headlight detection, headlight tracking and pairing, and vehicle classification (two-wheeled and four-wheeled vehicles). The novelty of our work is that headlights are validated and paired using trajectory tracing technique. The evaluation results are promising for a detection rate of 81.19% in nighttime scenes.
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References
S. V.-U. Ha, H.-H. Nguyen, H. M. Tran, and P. Ho-Thanh: Improved optical flow estimation in wrong way vehicle detection. Journal of Information Assurance and Security, vol. 9, no. 5, pp. 165–169 (2014).
B. Tian and B. T. Morris and M. Tang and Y. Liu and Y. Yao and C. Gou and D. Shen and S. Tang: Hierarchical and Networked Vehicle Surveillance in ITS: A Survey. in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 25–48 (2017).
Y. L. Chen, B. F. Wu, H. Y. Huang and C. J. Fan: A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance. IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2030–2044 (2011).
Z. S. Shifu Zhou, Jianxiong Li and L. Ying: A night time application for a real-time vehicle detection algorithm based on computer vision. Research Journal of Applied Sciences, Engineering and Technology (2013).
H. S.-Z. Habib Hajimolahoseini, Rassoul Amirfattahi: Robust vehicle tracking algorithm for nighttime videos captured by fixed cameras in highly reflective environments. IET Computer Vision (2014).
J. Rebut, B. Bradai, J. Moizard and A. Charpentier: A monocular vision based advanced lighting automation system for driving assistance. IEEE International Symposium on Industrial Electronics, Seoul, pp. 311–316 (2009).
P. F. Alcantarilla, L. M. Bergasa, P. Jimnez, I. Parra, D. F. Llorca, M. A. Sotelo, and S. S. Mayoral: Automatic LightBeam Controller for driver assistance. Journal of Machine Vision and Applications, vol. 22, no. 5, pp. 819–835 (2011).
R. O’malley, M. Glavin and E. Jones: Vision-based detection and tracking of vehicles to the rear with perspective correction in low-light conditions. IET Intelligent Transport Systems, vol. 5, no. 1, pp. 1–10 (2011).
J. C. Rubio, J. Serrat, A. M. Lopez and D. Ponsa: Multiple-Target Tracking for Intelligent Headlights Control. IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 594–605 (2012).
S. V.-U. Ha, L. H. Pham, H. M. Tran, and P. Ho-Thanh: Improved vehicles detection and classification algorithm for traffic surveillance system. Journal of Information Assurance and Security, vol. 9, no. 5, pp. 268–277 (2014).
Wei-zhi Wang and Bing-han Liu: The vehicle edge detection based on homomorphism filtering and fuzzy enhancement in night-time environments. IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, pp. 714–718 (2010).
L. H. Pham, T. T. Duong, H. M. Tran and S. V. U. Ha: Vision-based approach for urban vehicle detection and classification. Third World Congress on Information and Communication Technologies (WICT 2013), Hanoi, pp. 305–310 (2013).
T. M. Michell: Machine Leaning. McGraw Hill, Ch. 3, pp. 52–80 (1997).
M. Sokolova and G. Lapalme: A systematic analysis of performance measures for classification tasks. Information Processing and Management, vol. 45, no. 4, pp. 427–437 (2009).
Tom Fawcett: An introduction to ROC analysis. Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874 (2006).
D. M. W. Powers: Evaluation: from precision, recall and f-measure to roc., informedness, markedness and correlation. Journal of Machine Learning Technologies, vol. 2, pp.37–63 (2011).
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This research is funded by International University, Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number SV2016-IT-02.
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Vu, TA., Pham, L.H., Huynh, T.K., Ha, S.VU. (2018). Vehicle Classification in Nighttime Using Headlights Trajectories Matching. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_66
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DOI: https://doi.org/10.1007/978-981-10-7512-4_66
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