Kelly, Philip, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 and Smeaton, Alan F.
ORCID: 0000-0003-1028-8389
(2009)
Robust pedestrian detection and tracking in crowded scenes.
Image and Vision Computing, 27
(10).
pp. 1445-1458.
ISSN 0262-8856
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.
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | pedestrian detection; pedestrian tracking; stereo; crowds; |
Subjects: | Computer Science > Image processing |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering Research Institutes and Centres > CLARITY: The Centre for Sensor Web Technologies |
Publisher: | Elsevier |
Official URL: | http://dx.doi.org/10.1016/j.imavis.2008.04.006 |
Copyright Information: | Copyright © 2008 Elsevier B.V. |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Science Foundation Ireland, SFI 03/IN.3/I361 |
ID Code: | 4714 |
Deposited On: | 04 Feb 2010 13:37 by Philip Kelly . Last Modified 08 Nov 2018 16:08 |
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