Challenges of Embedded Computer Vision in Automotive Safety Systems
Vision-based automotive safety systems have received considerable attention over the past decade. Such systems have advantages compared to those based on other types of sensors such as radar, because of the availability of lowcost and high-resolution cameras and abundant information contained in video images. However, various technical challenges exist in such systems. One of the most prominent challenges lies in running sophisticated computer vision algorithms on low-cost embedded systems at frame rate. This chapter discusses these challenges through vehicle detection and classification in a collision warning system.
Unable to display preview. Download preview PDF.
- 1.A. Gern, U. Franke, P. Levi: Robust vehicle tracking fusing radar and vision. Proc. Int’l Conf. Multisensor Fusion and Integration for Intelligent Systems, 323–328 (2001).Google Scholar
- 2.B. Steus and C. Laurgeau and L. Salesse and D. Wautier: Fade: a vehicle detection and tracking system featuring monocular color vision and radar fusion. Proc. IEEE Intell. Veh. Symposium, 632–639 (2002).Google Scholar
- 5.Z. Sun, G. Bebis, R. Miller: On-road vehicle detection using Gabor filters and support vector machines. Proc. Int’l Conf. on Digital Signal Processing, 2, 1019–1022 (2002).Google Scholar
- 6.Z. Sun, G. Bebis, R. Miller: Improving the performance of on-road vehicle detection by combining Gabor and wavelet features. Proc. IEEE Int’l Conf. Intelligent Transportation Systems, 130–135 (2002).Google Scholar
- 7.M. Betke, E. Haritaglu, L. Davis: Multiple vehicle detection and tracking in hard real time. Proc. IEEE Intell. Veh. Symposium, 2, 351–356 (2006).Google Scholar
- 9.Y. Zhang, S. J. Kiselewich, W. A. Bauson: Legendre and Gabor moments for vehicle recognition in forward collision warning. Proc. IEEE Int’l Conf. Intelligent Transportation Systems, 1185–1190 (2006).Google Scholar
- 10.M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, T. Poggio: Pedestrian detection using wavelet templates. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, 193–199 (1997).Google Scholar
- 11.Y. Zhang, S. J. Kiselewich, W. A. Bauson: A monocular vision-based occupant classification approach for smart airbag deployment. Proc. IEEE Intell. Veh. Symposium, 632–637 (2005).Google Scholar
- 13.K. Levi, Y. Weiss: Learning object detection from a small number of examples: the importance of good features. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, 2, 53–60 (2004).Google Scholar
- 14.W. T. Freeman, M. Roth: Orientation histograms for hand gesture recognition. Proc. IEEE Int’l Workshop Automatic Face and Gesture Recognition, 296–301 (1995).Google Scholar
- 16.N. Dalal, B. Triggs: Histograms of oriented gradients for human detection. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, 1, 886–893 (2005).Google Scholar
- 21.I. Guyon, A. Elisseeff: An introduction to variable and feature selection. Journal of Machine Learning, 1157–1182 (2003).Google Scholar
- 22.P. Viola, M. J. Jones: Robust real-time face detection. Int’l Journal of Computer Vision, 57(2), 137–154 (2004).Google Scholar
- 23.R. Quinlan: See5: An Informal Tutorial. http://www.rulequest.com/see5-win.html. (2007).
- 25.Robert E. Schapire: The boosting approach to machine learning: an overview. MSRI Workshop on Nonlinear Estimation and Classification, (2002).Google Scholar