Work

Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks

Year: 2017

Type: conference-paper

Abstract: Pedestrian detection is a highly debated issue in the scientific community due to its outstanding importance for a large number of applications, especially in the fields of automotive safety, robotics and surveillance. In spite of the widely varying methods developed in recent years, pedestrian detection is still an open challenge whose accuracy and robustness has to be improved. Therefore, in this paper, we focus on improving the classification component in the pedestrian detection task on the Daimler stereo vision data set by adopting two approaches: 1) by combining three image modalities (intensity, depth and flow) to feed a unique convolutional neural network (CNN) and 2) by fusing the results of three independent CNNs.

Source: HAL

Authors: Pop, Danut Ovidiu, Rogozan, Alexandrina, Nashashibi, Fawzi, Bensrhair, Abdelaziz

Institutions: Babeș-Bolyai University, Institut National des Sciences Appliquées Rouen Normandie

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2605028682

FWCI: 0.773

Cites: 7

Cited by: 13

Related to: 10