2015/04/28 by Gorka Vélez, Velez, Gorka, Oihana Otaegui +1 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #Autonomous Vehicle Technology and Safety #C.3 #Computer Vision and Pattern Recognition (cs.CV) #D.4.7 #FOS: Computer and information sciences #I.2.10 #I.4.9 #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1504.07442
openalex publication_date 2015/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computer Vision, either alone or combined with other technologies such as\nradar or Lidar, is one of the key technologies used in Advanced Driver\nAssistance Systems (ADAS). Its role understanding and analysing the driving\nscene is of great importance as it can be noted by the number of ADAS\napplications that use this technology. However, porting a vision algorithm to\nan embedded automotive system is still very challenging, as there must be a\ntrade-off between several design requisites. Furthermore, there is not a\nstandard implementation platform, so different alternatives have been proposed\nby both the scientific community and the industry. This paper aims to review\nthe requisites and the different embedded implementation platforms that can be\nused for Computer Vision-based ADAS, with a critical analysis and an outlook to\nfuture trends.\n