@article{ref1,
title="Detection of Sudden Pedestrian Crossings for Driving Assistance Systems",
journal="IEEE transactions on systems, man, and cybernetics. Part B, cybernetics",
year="2011",
author="Xu, Yanwu and Xu, Dong and Lin, Stephen and Han, Tony X. and Cao, Xianbin and Li, Xuelong",
volume="42",
number="3",
pages="729-739",
abstract="In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.<p /> <p>Language: en</p>",
language="en",
issn="1083-4419",
doi="10.1109/TSMCB.2011.2175726",
url="http://dx.doi.org/10.1109/TSMCB.2011.2175726"
}