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.
Original languageEnglish
Title of host publicationThe 11th International Conference on Machine Vision
Number of pages8
Volume1104101
PublisherSPIE - International Society for Optical Engineering
Publication date15 Mar 2019
Pages110410I
DOIs
Publication statusPublished - 15 Mar 2019
EventThe 11th International Conference on Machine Vision - Munich, Germany
Duration: 1 Nov 20183 Nov 2018

Conference

ConferenceThe 11th International Conference on Machine Vision
CountryGermany
CityMunich
Period01/11/201803/11/2018

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Detectors
Neural networks

Cite this

Apostolopoulos, C., Nasrollahi, K., Yang, M-H. Y., Jahromi, M. N. S., & Moeslund, T. B. (2019). OCCLUSION-AWARE PEDESTRIAN DETECTION. In The 11th International Conference on Machine Vision (Vol. 1104101, pp. 110410I). SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2523107
Apostolopoulos, Christos ; Nasrollahi, Kamal ; Yang, Ming-Hsuan Yang ; Jahromi, Mohammad Naser Sabet ; Moeslund, Thomas B. / OCCLUSION-AWARE PEDESTRIAN DETECTION. The 11th International Conference on Machine Vision. Vol. 1104101 SPIE - International Society for Optical Engineering, 2019. pp. 110410I
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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.",
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Apostolopoulos, C, Nasrollahi, K, Yang, M-HY, Jahromi, MNS & Moeslund, TB 2019, OCCLUSION-AWARE PEDESTRIAN DETECTION. in The 11th International Conference on Machine Vision. vol. 1104101, SPIE - International Society for Optical Engineering, pp. 110410I, The 11th International Conference on Machine Vision, Munich, Germany, 01/11/2018. https://doi.org/10.1117/12.2523107

OCCLUSION-AWARE PEDESTRIAN DETECTION. / Apostolopoulos, Christos; Nasrollahi, Kamal; Yang, Ming-Hsuan Yang; Jahromi, Mohammad Naser Sabet; Moeslund, Thomas B.

The 11th International Conference on Machine Vision. Vol. 1104101 SPIE - International Society for Optical Engineering, 2019. p. 110410I.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AB - 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.

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Apostolopoulos C, Nasrollahi K, Yang M-HY, Jahromi MNS, Moeslund TB. OCCLUSION-AWARE PEDESTRIAN DETECTION. In The 11th International Conference on Machine Vision. Vol. 1104101. SPIE - International Society for Optical Engineering. 2019. p. 110410I https://doi.org/10.1117/12.2523107