Abstract
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.
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Zhang, Y., Dhua, A.S., Kiselewich, S.J., Bauson, W.A. (2009). Challenges of Embedded Computer Vision in Automotive Safety Systems. In: Kisačanin, B., Bhattacharyya, S.S., Chai, S. (eds) Embedded Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-304-0_13
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DOI: https://doi.org/10.1007/978-1-84800-304-0_13
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