Titlebook: Algorithm & SoC Design for Automotive Vision Systems; For Smart Safe Drivi Jaeseok Kim,Hyunchul Shin Book 2014 Springer Science+Business Me

別名

Image Enhancement for Improving Object Recognition,mprove the recognition rate of objects, several image enhancement algorithms are proposed and evaluated. In this chapter, general image enhancement techniques are introduced, followed by a discussion of advanced techniques for the driving environment.

Oration

Detection of Vehicles and Pedestrians,, different human postures/clothing, and weather/environment conditions is a challenging problem. In the following sections, the state of the art methods in vehicle and pedestrian detection are discussed.

Exonerate

https://doi.org/10.1007/978-3-031-24271-7mprove the recognition rate of objects, several image enhancement algorithms are proposed and evaluated. In this chapter, general image enhancement techniques are introduced, followed by a discussion of advanced techniques for the driving environment.

Spongy-Bone

遺傳學(xué)

Lens Correction and Gamma Correction,rection algorithm with objective illumination estimation method. In the lens correction part, we introduced a simple lens correction method in low-cost camera and we propose a method that leads to guarantee of the restrictions simultaneously for the determination. In the gamma correction part, we in

少量

,Monitoring Driver’s State and Predicting Unsafe Driving Behavior,ents such as PERCLOS, yawning detection and some limitations in measuring visual features are discussed in detail. As for non-visual features, we explore various physiological signals and possible drowsiness detection methods that use these signals. As for vehicle-based features, we describe steerin

ARCH

Hardware Accelerator for Feature Point Detection and Matching,core system. To overcome these limitations, we aims to implement hardware which achieve more than 90 frames per second in full HD resolution (1080p) only with 30?% of logic gates compared to the state-of-the-art object recognition processors. In this chapter, we introduces three techniques to design

Cuisine

Software Development Environment for Automotive SoC,e SoC is described. The automotive SoC consists of multiple processors, a customized vision process engine for an automotive vision system and peripheral. Each component of the SoC is mapped to the AUTOSAR layered architecture, such as MCAL, ECU abstraction and CCD.

殖民地

conspicuous

https://doi.org/10.1007/978-3-031-24271-7ents such as PERCLOS, yawning detection and some limitations in measuring visual features are discussed in detail. As for non-visual features, we explore various physiological signals and possible drowsiness detection methods that use these signals. As for vehicle-based features, we describe steerin