Abstract
Today’s traffic environment, such as traffic and information signs, road markings, and vehicles, is designed for human visual perception (even if first approaches for automatic evaluation by electronic sensor systems in the vehicle exist – see Chap. 50, “Intersection Assistance”). This is done by different shapes, colors, or a temporal change of the signals.
It is therefore a good choice to use a system similar to the human eye for machine perception of the environment. Camera systems are ideal candidates as they offer a comparable spectral, spatial, and temporal resolution. In addition to the “replica” of human vision, specific camera systems can provide other functions, including imaging in infrared spectral regions for night vision or a direct distance measurement.
This chapter covers details on specific applications of camera-based driver assistance systems and the resulting technical needs for the camera system. Use cases covering the outside and inside of the vehicle are shown. The basis of every camera system is the camera module with its main parts – the lens system and the image sensor. The underlying technology is described, and the formation of the camera image is discussed. Moving to the system level, basic camera architectures including mono and stereo systems are analyzed. The chapter is completed with a discussion of the calibration of camera systems.
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References
Brainard DH (1994) Bayesian method for reconstructing color images from trichromatic samples. In: Proceedings of the IS&T 47th annual meeting, Rochester, NY, pp. 375–380
Baxter D (2013) A line based HDR sensor simulator for motion artifact prediction. Proc SPIE 8653:86530F
Civera J, Bueno DR, Davison AJ, Montiel JMM (2009) Camera self-calibration for sequential bayesian structure from motion. In: Proceedings of the 2009 I.E. international conference on Robotics and Automation, Kobe, Japan
Daimler (2014) Homepage Daimler. http://www.mercedes-benz.com. Accessed 10 Jan 2014
Darmont A (2012) High dynamic range imaging, sensors and architectures. SPIE Press, Washington
Delphi (2014) Homepage Delphi. http://delphi.com/manufacturers/auto/safety/active/racam/. Accessed 10 Jan 2014
El Gamal A, Eltoukhy H (2005) CMOS image sensors. IEEE Circuits Devices Mag 21(3):6
Fiete RD (2010) Modelling the imaging chain of digital cameras. SPIE Press, Bellingham
Fischer RE (2008) Optical system design. McGraw-Hill, New York
Gentex (2014) Homepage Gentex. http://www.gentex.com/automotive/products/forward-driving-assist. Accessed 10 Jan 2014
Hecht E (1998) Optics. Addison Wesley Longman, New York
Hertel D (2010) Extended use of incremental signal-to-noise ratio as reliability criterion for multiple-slope wide-dynamic-range image capture. J Electron Imaging 19(1):011007
Holst GC, Lomheim TS (2011) CMOS/CCD sensors and camera systems. SPIE Press, Washington
ISO 26262: Road vehicles – functional safety
ISO/DIS 16505: Road vehicles – ergonomic and performance aspects of camera-monitor systems – requirements and test procedures
Källhammer J (2006) Night vision: requirements and possible roadmap for FIR and NIR systems. Proc SPIE 6198:61980F
Loce RP, Berna I, Wu W, Bala R (2013) Computer vision in roadway transportation systems: a survey. J Electron Imaging 22(4):041121
Miller JWY, Murphey YL, Khairallah F (2004) Camera performance considerations for automotive applications. Proc SPIE 5265:163
MIPI (2014) Homepage MIPI-Alliance. http://www.mipi.org/specifications/camera-interface. Accessed 10 Jan 2014
Nakamura J (2006) Image sensors and signal processing for digital still cameras. CRC Press, Boca Raton
Raphael E, Kiefer R, Reisman P, Hayon G (2011) Development of a camera-based forward collision alert system. SAE Int J Passenger Cars Mech Syst 4(1):467
Reinhard E, Khan EA, Akyüz AO, Johnson GM (2008) Color imaging. A.K. Peters, Wellesley
Sinha PK (2012) Image acquisition and preprocessing for machine vision systems. SPIE Press, Washington
Solhusvik J, Yaghmai S, Kimmels A, Stephansen C, Storm A, Olsson J, Rosnes A, Martinussen T, Willassen T, Pahr PO, Eikedal S, Shaw S, Bhamra R, Velichko S, Pates D, Datar S, Smith S, Jiang L, Wing D, Chilumula A (2009) A 1280 × 960 3.75um pixel CMOS imager with triple exposure HDR. In: Proceedings of 2009 international image sensor workshop, Utah, USA
Solhusvik J, Kuang J, Lin Z, Manabe S, Lyu J, Rhodes H (2013) A comparison of high dynamic range CIS technologies for automotive applications. In: Proceedings of 2013 international image sensor workshop, Utah, USA
Stein GP, Gat I, Hayon G (2008) Challenges and solutions for bundling multiple DAS applications on a single hardware platform. Israel computer vision day
Theuwissen AJP (2008) Course “digital camera systems” – hand out. CEI.se, Finspong
Tsai RY (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom 3(4):323
Yadid-Pecht O, Etienne-Cummings R (2004) CMOS imagers: from phototransduction to image processing. Kluwer, Dordrecht
Zhang Z (1998) Determining the epipolar geometry and its uncertainty: a review. Int J Comput Vis 27(2):161
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Punke, M., Menzel, S., Werthessen, B., Stache, N., Höpfl, M. (2016). Automotive Camera (Hardware). In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds) Handbook of Driver Assistance Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-12352-3_20
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DOI: https://doi.org/10.1007/978-3-319-12352-3_20
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