Titlebook: Computer Vision for Driver Assistance; Simultaneous Traffic Mahdi Rezaei,Reinhard Klette Book 2017 Springer International Publishing AG 201

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cardiovascular

Samuel H. Preston,Irma T. Elo,Ira Rosenwaikele at all times, but provided automated control features of the vehicle (based on input data generated by different sensors) already enhance safety and driver comfort. We especially consider automated control features possible by using camera data.

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老人病學(xué)

https://doi.org/10.1007/978-90-481-8978-6ised and unsupervised learning approaches. The chapter provides technical details for each method, discussions on the strengths and weaknesses of each method, and gives examples and various applications for each method. Material is provided to support a decision for an appropriate object detection t

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Liability

Data Sources for Health Demography,g method and an accurate 2D-to-3D registration technique to obtain the driver’s head pose, yawing detection, and head-nodding detection. Chapter . and this chapter present the first major objective of this book’s focus on “driver behaviour” (i.e. driver drowsiness and distraction detection). The fin

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孤僻

不溶解

https://doi.org/10.1007/978-3-319-50551-0advanced driver-assistance systems; autonomous vehicles; driver distraction; driver fatigue; vehicle det