Abstract
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.
Highlights
Pedestrian protection is a key problem in the context of the automotive industry and its applications.Sensor systems onboard the vehicles are required for predicting the vehicle host-to-pedestrian (H2P)distance as wells as the time-to-collision (TTC)
In this paper we present 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 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
Summary
Pedestrian protection is a key problem in the context of the automotive industry and its applications. An overview of the current state of the art from both a methodological and an experimental perspective is presented in [6], where a novel benchmark set has been made publicly available We refer to these surveys for general and detailed background concerning pedestrian detection for automotive applications. In this paper we present 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 performed field test provided encouraging results and proved the validity of the proposed sensor concerning the accuracy required in one of the most challenging and difficult applications in the context of the automotive industry.
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