How Far Are We From Solving Pedestrian Detection?

Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1259-1267

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 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 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 dataset, and provide a new sanitised set of training and test annotations.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhang_2016_CVPR,
author = {Zhang, Shanshan and Benenson, Rodrigo and Omran, Mohamed and Hosang, Jan and Schiele, Bernt},
title = {How Far Are We From Solving Pedestrian Detection?},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}