title: Detection of sudden pedestrian crossings for driving assistance systems.
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.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. ywxu@ntu.edu.sg
authors.forename: Yanwu
authors.initials: Y
authors.lastname: Xu
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.forename: Stephen
authors.initials: S
authors.lastname: Lin
authors.forename: Tony X
authors.initials: TX
authors.lastname: Han
authors.forename: Xianbin
authors.initials: X
authors.lastname: Cao
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D000465:Algorithms; D001185:Artificial Intelligence; D001334:Automobile Driving; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*