Journal
IMAGE ANALYSIS AND RECOGNITION, ICIAR 2017
Volume 10317, Issue -, Pages 268-277Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59876-5_30
Keywords
Feature extraction; Ground plane segmentation; Pedestrian detection
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This paper presents a method of ground plane segmentation for urban outdoor scenes using a feedforward artificial neural network (ANN). The main motivation of this project is to obtain some contextual information from the scene for use in pedestrian detection algorithms and to provide an accuracy improvement for this algorithms. The ANN input is fed with features extracted from a patch window of the image scene. The ANN output classifies the patch as belonging or not belonging to the ground plane. After that, the classified patches are joined into a full image with the ground plane area outlined. The images used for training, test and evaluation were obtained from the widely known Caltech-USA database. The accuracy of ground plane segmentation was above 96% in the experiments which improved the precision of the pedestrian detector in 38,5%.
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