@article{xu2011detection,
author = {Xu, Yanwu and Xu, Dong and Lin, Steve and Han, Tony X. and Cao, Xiambin and Li, Xuelong},
title = {Detection of Sudden Pedestrian Crossings for Driving Assistance Systems},
year = {2011},
month = {November},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/detection-sudden-pedestrian-crossings-driving-assistance-systems/},
pages = {729-739},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
volume = {42},
number = {3},
}