@article{ref1,
title="Far-infrared based pedestrian detection for driver-assistance systems based on candidate filters, gradient-based feature and multi-frame approval matching",
journal="Sensors (Basel)",
year="2015",
author="Wang, Guohua and Liu, Qiong",
volume="15",
number="12",
pages="32188-32212",
abstract="Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians' head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians' size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s151229874",
url="http://dx.doi.org/10.3390/s151229874"
}