Vision for Driver Assistance: Looking at People in a Vehicle


An important real-life application domain of computer vision techniques looking at people is in developing Intelligent Driver Assistance Systems (IDAS’s). By analyzing information from both looking in and looking out of the vehicle, such systems can actively prevent vehicular accidents, improve driver safety as well as driver experience. Towards such goals, developing systems looking people in a vehicle (i.e. driver and passengers) to understand their intent, behavior, and states is needed. This is a challenging task which typically requires high reliability, accuracy, and efficient performance. Challenges also come from the dynamic background and varying lighting condition in driving scenes. However, looking at people in a vehicle also has its own characteristics which could be exploited to simplify the problem such as people typically sitting in a fixed position and their activities being highly related to the driving context. In this chapter, we give a concise overview of various related research studies to see how their approaches were developed to fit the specific requirements and characteristics of looking at people in a vehicle. From a historical point of view, we first discuss studies looking at head, eyes, and facial landmarks and then studies looking at body, hands, and feet. Despite lots of active research and published papers, developing accurate, reliable, and efficient approaches for looking at people in real-world driving scenarios is still an open problem. To this end, we will discuss some remaining issues for the future development in the area.


Particle Filter Controller Area Network Facial Landmark Computer Vision Technique National Highway Traffic Safety Administration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the sponsorships of U.C. Discovery Program, National Science Foundation as well as industry sponsors including Nissan, Volkswagen Electronic Research Laboratory, and Mercedes. We also thank former and current colleagues from our Laboratory for Intelligent and Safe Automobiles (LISA) for their cooperation, assistance, and contributions: Dr. Kohsia Huang, Dr. Joel McCall, Dr. Tarak Gandhi, Dr. Sangho Park, Dr. Shinko Cheng, Dr. Steve Krotosky, Dr. Junwen Wu, Dr. Erik Murphy-Chutorian, Dr. Brendan Morris, Dr. Anup Doshi, Mr. Sayanan Sivaraman, Mr. Ashish Tawari, and Mr. Ofer Achlertheir.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Laboratory for Intelligent and Safe Automobiles (LISA)University of California at San DiegoSan DiegoUSA

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