Cao, X. and Wang, Z. and Yan, P. and Li, Xuelong (2011) Rapid pedestrian detection in unseen scenes. Neurocomputing 74 (17), pp. 3343-3350. ISSN 0925-2312.
Abstract
In this paper, a rapid adaptive pedestrian detection method based on cascade classifier with ternary pattern is proposed. The proposed method achieves its goal by employing the following three new strategies: (1) A method for adjusting the key parameters of the trained cascade classifier dynamically for detecting pedestrians in unseen scenes using only a small amount of labeled data from the new scenes. (2) An efficient optimization method is proposed, based on the cross entropy method and a priori knowledge of the scenes, to solve the classifier parameter optimization problem. (3) In order to further speed up pedestrian detection in unseen scenes, each strong classifier in the cascade employs a ternary detection pattern. In our experiments, two significantly different datasets, AHHF and NICTA, were used as the training set and testing set, respectively. The experimental results showed that the proposed method can quickly adapt a previously trained detector for pedestrian detection in various scenes compared with other existing methods.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | pedestrian detection system, adaptive detector, cascade classifier, cross entropy, ternary detection pattern |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 20 Jun 2013 09:32 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7510 |
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