@article{ref1,
title="Robust pedestrian detection and tracking in crowded scenes",
journal="Image and vision computing",
year="2009",
author="Kelly, Philip and O'Connor, Noel E. and Smeaton, Alan F.",
volume="27",
number="10",
pages="1445-1458",
abstract="In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan-view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases.<p />",
language="",
issn="0262-8856",
doi="10.1016/j.imavis.2008.04.006",
url="http://dx.doi.org/10.1016/j.imavis.2008.04.006"
}