Optimising Computer Vision Based ADAS: Vehicle Detection Case Study

Computer vision methods for advanced driver assistance systems (ADAS) must be developed considering the strong requirements imposed by the industry, including real-time performance in low cost and low consumption hardware (HW), and rapid time to market. These two apparently contradictory requirements create the necessity of adopting careful development methodologies. In this study the authors review existing approaches and describe the methodology to optimize computer vision applications without incurring in costly code optimization or migration into special HW. This approach is exemplified on the improvements achieved on the successive re-designs of vehicle detection algorithms for monocular systems. In the experiments the authors observed a 15-fold speed up between the first and fourth prototypes, progressively optimized using the proposed methodology from the very first naive approach to a fine-tuned algorithm.

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  • Supplemental Notes:
    • Abstract reprinted with permission of the Institution of Engineering and Technology.
  • Authors:
    • Nieto, Marcos
    • Velez, Gorka
    • Otaegui, Oihana
    • Gaines, Sean
    • Van Cutsem, Geoffroy
  • Publication Date: 2016-4

Language

  • English

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  • Accession Number: 01603726
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 29 2016 9:38AM