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

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.
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Contributor : Alexandrina Rogozan <>
Submitted on : Thursday, May 2, 2019 - 1:19:22 PM
Last modification on : Tuesday, August 11, 2020 - 11:40:24 AM

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Bassem Besbes, Alexandrina Rogozan, Adela-Maria Rus, Abdelaziz Bensrhair, Alberto Broggi. Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF. Sensors, MDPI, 2015, 15 (4), pp.8570-8594. ⟨10.3390/s150408570⟩. ⟨hal-02117486⟩



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