Ground plane segmentation using artificial neural network for pedestrian detection

Candido J.; Marengoni M.

Ground plane segmentation using artificial neural network for pedestrian detection

Tipo

Artigo de evento

Data de publicação

2017

Periódico

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Citações (Scopus)

2

Autores

Candido J.
Marengoni M.

Resumo

© Springer International Publishing AG 2017.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%.

Assuntos Scopus

Accuracy Improvement , Contextual information , Feed-forward artificial neural networks , Ground planes , Image scene , Pedestrian detection , Test and evaluation , Urban outdoor