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Sensors 2015, 15(4), 8570-8594; doi:10.3390/s150408570

Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF

1
Diotasoft, 15 Boulevard Emile Baudot, Massy 91300, France
2
LITIS Laboratory, National Institute of Applied Sciences, 76801 Saint-Etienne-du-Rouvray Cedex, France
3
Faculty of Computer Science, Babes-Bolyai University, Kogalniceanu no.1, Cluj-Napoca RO-400084, Romania
4
Dipartimento di Ingegneria dell' Informazione, Universita di Parma, Parco Area delle Scienze, Parma 181/a 43124, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 7 February 2015 / Revised: 2 April 2015 / Accepted: 7 April 2015 / Published: 13 April 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)
View Full-Text   |   Download PDF [3204 KB, uploaded 13 April 2015]   |  

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. View Full-Text
Keywords: pedestrian detection; far-infrared images; scale-invariant feature matching; SURF; hierarchical codebook; SVM; pedestrian classification and tracking pedestrian detection; far-infrared images; scale-invariant feature matching; SURF; hierarchical codebook; SVM; pedestrian classification and tracking
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Besbes, B.; Rogozan, A.; Rus, A.-M.; Bensrhair, A.; Broggi, A. Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF. Sensors 2015, 15, 8570-8594.

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