Towards Reaching Human Performance in Pedestrian Detection

Zhang, Shanshan and Benenson, Rodrigo and Omran, Mohamed and Hosang, Jan and Schiele, Bernt
(2017) Towards Reaching Human Performance in Pedestrian Detection.
IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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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