Optimising computer vision based ADAS: vehicle detection case study

Marcos Nieto; Gorka Vélez; Oihana Otaegui; Seán Gaines; Geoffroy Van Cutsem

Optimising computer vision based ADAS: vehicle detection case study

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Author(s): Marcos Nieto ¹ ; Gorka Vélez ¹ ; Oihana Otaegui ¹ ; Seán Gaines ¹ ; Geoffroy Van Cutsem ²
- Affiliations: 1: Vicomtech-IK4, Paseo Mikeletegi 57, San Sebastian, Spain ;
  2: Intel Corporation SA, Veldkant, 31, 2550 Kontich, Belgium
Source: Volume 10, Issue 3, April 2016, p. 157 – 164
DOI: 10.1049/iet-its.2014.0303 , Print ISSN 1751-956X, Online ISSN 1751-9578

Received 16/12/2014, Accepted 23/09/2015, Revised 20/08/2015, Published 01/04/2016

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 optimise computer vision applications without incurring in costly code optimisation 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 speed up between the first and fourth prototypes, progressively optimised using the proposed methodology from the very first naive approach to a fine-tuned algorithm.

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Optimising computer vision based ADAS: vehicle detection case study

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