TY - JOUR

T1 - Online stereo camera calibration for automotive vision based on HW-accelerated A-KAZE-feature extraction

AU - Mentzer, Nico

AU - Mahr, Jannik

AU - Payá-Vayá, Guillermo

AU - Blume, Holger

N1 - © 2018 Elsevier B.V. All rights reserved.

PY - 2019/8

Y1 - 2019/8

N2 - Nowadays ongoing integration of camera based advanced driver assistance systems (ADAS) in vehicles demands increasingly complex digital image processing in order to interpret the surrounding situations. To ensure a timely reaction for obstacles lying ahead, far reaching depth information of the observed scene is necessary. Using stereo camera systems, this is achievable by enlarging the camera base line. With rapid driving, the exact alignment of the stereo images is no longer ensured due to vibrations of the vehicle. Based on detection, extraction and matching of Accelerated-KAZE image features (A-KAZE), the geometric distortions are compensable by estimating the external camera parameters for image rectification. The indispensable frame rate for applications in vehicles and the limited power budget in combination with the SW-flexibility demanded for future ADAS applications requires the usage of optimized hardware architectures. Thus, an online camera calibration based on an HW-accelerated A-KAZE extraction is introduced in this work. The suitability of A-KAZE features for an online camera calibration is proven. Furthermore, the Tensilica Vision P5-processor is evaluated regarding its suitability for real-time A-KAZE feature extraction. This processor provides a comprehensive instruction-set extension for high-performance digital image processing. While preserving the initial A-KAZE accuracy, a feature descriptor length reduction of factor  × 3.8 is attained compared to the initial descriptor size and an estimated frame rate of 20 fps is achieved for A-KAZE feature extraction on the Tensilica Vision P5-processor.

AB - Nowadays ongoing integration of camera based advanced driver assistance systems (ADAS) in vehicles demands increasingly complex digital image processing in order to interpret the surrounding situations. To ensure a timely reaction for obstacles lying ahead, far reaching depth information of the observed scene is necessary. Using stereo camera systems, this is achievable by enlarging the camera base line. With rapid driving, the exact alignment of the stereo images is no longer ensured due to vibrations of the vehicle. Based on detection, extraction and matching of Accelerated-KAZE image features (A-KAZE), the geometric distortions are compensable by estimating the external camera parameters for image rectification. The indispensable frame rate for applications in vehicles and the limited power budget in combination with the SW-flexibility demanded for future ADAS applications requires the usage of optimized hardware architectures. Thus, an online camera calibration based on an HW-accelerated A-KAZE extraction is introduced in this work. The suitability of A-KAZE features for an online camera calibration is proven. Furthermore, the Tensilica Vision P5-processor is evaluated regarding its suitability for real-time A-KAZE feature extraction. This processor provides a comprehensive instruction-set extension for high-performance digital image processing. While preserving the initial A-KAZE accuracy, a feature descriptor length reduction of factor  × 3.8 is attained compared to the initial descriptor size and an estimated frame rate of 20 fps is achieved for A-KAZE feature extraction on the Tensilica Vision P5-processor.

KW - A-KAZE

KW - Advanced driver assistance systems

KW - ASIP

KW - Feature matching

KW - Tensilica vision P5

U2 - 10.1016/j.sysarc.2018.11.003

DO - 10.1016/j.sysarc.2018.11.003

M3 - Article

AN - SCOPUS:85056995316

VL - 97

SP - 335

EP - 348

JO - Journal of Systems Architecture

JF - Journal of Systems Architecture

SN - 1383-7621

ER -