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Proceedings Paper

Occlusion-aware pedestrian detection
Author(s): Christos Apostolopoulos; Kamal Nasrollahi; M. Hsuan Yang; Mohammad N. S. Jahromi; Thomas B. Moeslund
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Paper Abstract

Failure in pedestrian detection systems can be extremely crucial, specifically in driverless driving. In this paper, failures in pedestrian detectors are refined by re-evaluating the results of state of the art pedestrian detection systems, via a fully convolutional neural network. The network is trained on a number of datasets which include a custom designed occluded pedestrian dataset to address the problem of occlusion. Results show that when applying the proposed network, detectors can not only maintain their state of the art performance, but they even decrease average false positives rate per image, especially in the case where pedestrians are occluded.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410I (15 March 2019); doi: 10.1117/12.2523107
Show Author Affiliations
Christos Apostolopoulos, Aalborg Univ. (Denmark)
Kamal Nasrollahi, Aalborg Univ. (Denmark)
M. Hsuan Yang, Univ. of California, Merced (United States)
Mohammad N. S. Jahromi, Aalborg Univ. (Denmark)
Thomas B. Moeslund, Aalborg Univ. (Denmark)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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