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Robust pedestrian detection and tracking in crowded scenes

Kelly, Philip and O'Connor, Noel E. and Smeaton, Alan F. (2009) Robust pedestrian detection and tracking in crowded scenes. Image and Vision Computing, 27 (10). pp. 1445-1458. ISSN 0262-8856

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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)
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 Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
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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 29 Apr 2010 13:36

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