This paper describes a real-time vision-based system that detects vehicles approaching from the rear in order to anticipate possible rear-end collisions. A camera mounted on the rear of the vehicle provides images which are analysed by means of computer vision techniques. The detection of candidates is carried out using the top-hat transform in combination with intensity and edge-based symmetries. The candidates are classified by using a Support Vector Machine-based classifier (SVM) with Histograms of Oriented Gradients (HOG features). Finally, the position of each vehicle is tracked using a Kalman filter and template matching techniques. The proposed system is tested using image data collected in real traffic conditions. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Balcones, D., Llorca, D. F., Sotelo, M. A., Gavilán, M., Álvarez, S., Parra, I., & Ocaña, M. (2009). Real-time vision-based vehicle detection for rear-end collision mitigation systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5717 LNCS, pp. 320–325). https://doi.org/10.1007/978-3-642-04772-5_42
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