@article{ref1,
title="Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF",
journal="Sensors (Basel)",
year="2015",
author="Besbes, Bassem and Rogozan, Alexandrina and Rus, Adela-Maria and Bensrhair, Abdelaziz and Broggi, Alberto",
volume="15",
number="4",
pages="8570-8594",
abstract="One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s150408570",
url="http://dx.doi.org/10.3390/s150408570"
}