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キーワード:
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要旨:
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.
