Robust pedestrian detection and tracking in crowded scenes
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.