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  Towards Reaching Human Performance in Pedestrian Detection

Zhang, S., Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2018). Towards Reaching Human Performance in Pedestrian Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 973-986. doi:10.1109/TPAMI.2017.2700460.

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 Creators:
Zhang, Shanshan1, Author           
Benenson, Rodrigo1, Author           
Omran, Mohamed1, Author           
Hosang, Jan1, Author           
Schiele, Bernt1, Author           
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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 Abstract: Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the “perfect single frame detector”. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations.

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Language(s): eng - English
 Dates: 2017-05-022018
 Publication Status: Issued
 Pages: 14 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ZBOHS2017
DOI: 10.1109/TPAMI.2017.2700460
 Degree: -

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Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  Other : IEEE Trans. Pattern Anal. Mach. Intell.
  Abbreviation : TPAMI
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Los Alamitos, CA : IEEE Computer Society
Pages: - Volume / Issue: 40 (4) Sequence Number: - Start / End Page: 973 - 986 Identifier: ISSN: 0162-8828
CoNE: https://pure.mpg.de/cone/journals/resource/954925479551