Intelligent vision processing technology has a wide range of applications on vehicles. Many of these applications are related to a so-called Advanced Driver Assistance System (ADAS). Collaborated with cameras, Pedestrian and Motorcyclist Detection System (PMD), Lane Departure Warning System (LDWS), Forward Collision Warning System (FCWS), Speed Limit Detection System (SLDS), and Dynamic Local Contrast Enhancement (DLCE) techniques can help drivers notice important events or objects around. This chapter gives an in-depth exploration for these intelligent vision processing technologies from the viewpoints of methodology development, algorithm optimization, and system implementation on embedded platforms. More precisely, this chapter tends to first give a survey and overview for newly appeared state-of-the-art intelligent vision processing technologies for ADAS, and then highlights some significant technologies including PMD, LDWS, FCWS, SLDS, and DLCE developed in System on Chip (SoC) Laboratory, Fong- Chia University, Taiwan, and intelligent Vision System (iVS) Laboratory, National Chiao Tung University, Taiwan. Besides, implementation and verification of the above ADAS technologies will also be presented. In summary, the proposed PMD design achieves 32.5 frame per second (fps) for 720×480 (D1) resolution on an AMD A10-7850K processor by using heterogeneous computing. On an automotive-grade Freescale i.MX6 (including 4-core ARM Cortex A9, 1 GB DDR3 RAM, and Linux environment) platform, the proposed LDWS, FCWS, and SLDS designs, respectively, achieve 33 fps, 32 fps, and 30 fps for D1 resolution. Finally, the proposed DLCE system is realized on a TREK-668 platform with an Intel Atom 1.6 GHz processor for real-time requirement of 50 fps at D1 resolution.
|Title of host publication||Smart Sensors and Systems|
|Subtitle of host publication||Innovations for Medical, Environmental, and IoT Applications|
|Publisher||Springer International Publishing|
|Number of pages||32|
|State||Published - 1 Jan 2016|
- Intelligent vision processing
- Pedestrian detection