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Towards Reaching Human Performance in Pedestrian Detection

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons134279

Zhang,  Shanshan
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79212

Benenson,  Rodrigo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons136609

Omran,  Mohamed
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86681

Hosang,  Jan
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Zitation

Zhang, S., Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2017). Towards Reaching Human Performance in Pedestrian Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2017.2700460.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002D-440B-2
Zusammenfassung
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.