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
Full text available as:
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.
Archive Staff Only: edit this record