Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-24
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment.
In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road.
To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure.
The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset.
Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.
American Psychological Association (APA)
Thu, M.& Suvonvorn, N.. 2020. Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification. Advances in Multimedia،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1126703
Modern Language Association (MLA)
Thu, M.& Suvonvorn, N.. Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification. Advances in Multimedia No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1126703
American Medical Association (AMA)
Thu, M.& Suvonvorn, N.. Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification. Advances in Multimedia. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1126703
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126703