Journal
SENSORS
Volume 10, Issue 4, Pages 3741-3758Publisher
MDPI
DOI: 10.3390/s100403741
Keywords
3D sensors; automotive industry; computer vision; stereo quantization errors; pedestrian detection
Funding
- Spanish Ministry of Science and Innovation [TRA2008-06602-C03]
- Spanish Ministry of Development [GUIADE P9/08]
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This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance.
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