Abstract
This paper presents an improved method for detecting and segmenting taillight pairs of multiple preceding cars in busy traffic in day as well as night. Novelties and advantages of this method are that it is designed to detect multiple car simultaneously, it does not require knowledge of lanes, it works in busy traffic in daylight as well as night, and it is fast irrespective of number of preceding vehicles in the scene, and therefore suitable for real-time applications. The time to process the scene is independent of the size of the vehicle in pixels, and the number of preceding cars detected.
One of the previous night taillight detection methods in literature is modified to detect taillight pairs in the scene for both day and night conditions. This paper further introduces a novel hypothesis verification method based on the mathematical relationship between the vehicle distance from the vanishing point and the location of and distance between its taillights. This method enables the detection of multiple preceding vehicles in multiple lanes in a busy traffic environment in real-time. The results are compared with state-of-the-art algorithms for preceding vehicle detection performance, time and ease of implementation.
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References
Chen, D.-Y., Lin, Y.-H.: Frequency-tuned nighttime brake-light detection. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 619–622 (2010)
Crisman, J.D., Thorpe, C.E.: Color vision for road following. In: Thorpe, C. (ed.) Vision and Navigation: The CMU Navlab, pp. 9–24 (1988)
Du, Y., Papanikolopoulos, N.P.: Real-time vehicle following through a novel symmetry-based approach. In: IEEE International Conference on Robotics and Automation, vol. 4, pp. 3160–3165 (April 1997)
Fu, C.-M., Huang, C.-L., Chen, Y.-S.: Vision-based preceding vehicle detection and tracking. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 2, pp. 1070–1073 (September 2006)
Gupta, R.A., Snyder, W.E., Pitts, W.S.: Concurrent visual multiple lane detection for autonomous vehicles. In: Proceedings of the IEEE International Conference on Robotics and Automation (May 2010)
Ito, T., Yamada, K.: Preceding vehicle and road lanes recognition methods for rcas using vision system. In: Proceedings of the Intelligent Vehicles Symposium, October 24-26, pp. 85–90 (1994)
Jiangwei, C., Lisheng, J., Lie, G., Libibing, Rongben, W.: Study on method of detecting preceding vehicle based on monocular camera. In: IEEE Intelligent Vehicles Symposium, June 14-17, pp. 750–755 (2004)
Kuehni, R.G.: Color Space and Its Divisions: Color Order from Antiquity to the present. Wiley, New York (2003)
O’Malley, R., Glavin, M., Jones, E.: Vehicle detection at night based on tail-light detection. In: 1st International Symposium on Vehicular Computing Systems (July 2008)
Snyder, W.E., Savage, C.D.: Content-addressable read/write memories for image analysis. IEEE Transactions on Computers C-31(10), 963–968 (1982)
Sukthankar, R.: Raccoon: A real-time autonomous car chaser operating optimally at night. In: Proceedings of IEEE Intelligent Vehicles (1993)
Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection using evolutionary gabor filter optimization. IEEE Transactions on Intelligent Transportation Systems 6(2), 125–137 (2005)
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Gupta, R.A., Snyder, W.E. (2011). Detection of Multiple Preceding Cars in Busy Traffic Using Taillights. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_34
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DOI: https://doi.org/10.1007/978-3-642-21596-4_34
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