  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 dataset), and by manually clustering the recurrent
errors of a top detector. Our results characterize both localization and
background-versus-foreground errors. To address localization errors we study
the impact of training annotation noise on the detector performance, and show
that we can improve even with a small portion of sanitized 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 dataset, and
provide a new sanitized set of training and test annotations.
