How Far are We from Solving Pedestrian Detection?

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 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.

Paper on arXiv.

@INPROCEEDINGS{Shanshan2016CVPR,
  Author = {Shanshan Zhang and Rodrigo Benenson and Mohamed Omran and Jan Hosang and Bernt Schiele},
  Title = {How Far are We from Solving Pedestrian Detection?},
  Year = {2016},
  Booktitle = {CVPR}
 }