(2017) Towards Reaching Human Performance in 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 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.
| Item Type: | Article |
|---|---|
| Depositing User: | Sebastian Weisgerber |
| Date Deposited: | 22 Feb 2018 11:11 |
| Last Modified: | 22 Feb 2018 14:33 |
| URI: | https://publications.cispa.saarland/id/eprint/1859 |
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