@article{ref1,
title="Pedestrian detection and tracking in infrared imagery using shape and appearance",
journal="Computer vision and image understanding",
year="2007",
author="Dai, Congxia and Zheng, Yunfei and Li, Xin",
volume="106",
number="2-3",
pages="288-299",
abstract="In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appear- ance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to sep- arate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multi- ple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance informa- tion. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.<p />",
language="",
issn="1077-3142",
doi="10.1016/j.cviu.2006.08.009",
url="http://dx.doi.org/10.1016/j.cviu.2006.08.009"
}