TY - JOUR AU - Alejandro Gonzalez Alzate AU - Zhijie Fang AU - Yainuvis Socarras AU - Joan Serrat AU - David Vazquez AU - Jiaolong Xu AU - Antonio Lopez PY - 2016// TI - Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison T2 - SENS JO - Sensors SP - 820 VL - 16 IS - 6 KW - Pedestrian Detection KW - FIR N2 - Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset. SN - 1424-8220 L1 - http://refbase.cvc.uab.es/files/GFS2016.pdf UR - http://dx.doi.org/10.3390/s16060820 N1 - ADAS; 600.085; 600.076; 600.082; 601.281 ID - Alejandro Gonzalez Alzate2016 ER -