Abstract
This study suggests a method to detect multiple vehicles, which is important for driving assistance system. In a frame of color image, shadow information and edge elements are used to detect vehicle candidate areas. Detecting the areas of multiple vehicles requires to analyze Estimation of Vehicle (EOV) and Accumulated Similarity Function (ASF) from the vehicle candidate areas that exist in image sequences. Later by evaluating the possibility of vehicles, it determines the vehicle areas. Most studies focus on detecting a single vehicle in front. This study, however, focuses on detecting multiple vehicles even in heavy traffic and frequent change of lanes.
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References
Ernst, S., Stiller, C., Goldbeck, J., Roessig, C.: Camera Calibration for Lane and Obstacle Detection. In: Proceedings of the International Conference on Intelligent Transportation Systems, pp. 356–361 (1999)
Tsunashima, N., Nakajima, M.: Extraction of the Front Vehicle using Projected Disparity Map. In: Conference Visual Communications and Image Processing 1999, California, January 1999, pp. 1297–1304 (1999)
Lissel, E., Andreas, P., Bergholz, R., Weisser, H.: From Automatic Distance Regulation to Collision Avoidance. In: International Symposium on Avoidance Vehicle Control, AVEC 1996, pp. 1367–1378 (1996)
Smith, S.M., Brady, J.M.: ASSET-2: Real-Time Motion Segmentation and Shape Tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 17(8), 814–820 (1995)
Werner, M., van Seelen, W.: An Image processing system for assistance. Image and Vision Computing, pp. 367–376 (2000)
De Micheli, E., Prevete, R., Piccioli, G., Campani, M.: Color cues for traffic scene analysis. In: Intelligent Vehicles 1995 Symposium, pp. 466–471 (1995)
Betke, M., Haritaoglu, E., Davis, L.S.: Highway Scene Analysis in Hard Real-Time. Intelligent Transportation System, 812–817 (1997)
Heisele, B., Ritter, W.: Obstacle Detection Based On Color Blob Flow. In: Proceedings of the Intelligent Vehicles 1995 Symposium, pp. 282–286 (1995)
Shafer, S.A.: Using color to separate reflection components. Color research and application 10(4), 210–218 (1985)
Han, S., Cho, H.: HSV Color Model Based Front Vehicle Extraction and Lane Detection using Shadow Information. Journal of Korea Multimedia Society 5(2), 176–190 (2002)
Du, Y., Papanikolopoulos, N.P.: Real-time vehicle following through a novel ymmetry-based approach. In: Proceedings of Robotics and Automation, vol. 4, pp. 3160–3165 (1997)
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Han, S., Ahn, E., Kwak, N. (2005). Detection of Multiple Vehicles in Image Sequences for Driving Assistance System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_117
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DOI: https://doi.org/10.1007/11424758_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25860-5
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