TY  - JOUR
PY  - 2018//
TI  - Towards reaching human performance in pedestrian detection
JO  - IEEE transactions on pattern analysis and machine intelligence
A1  - Zhang, Shanshan
A1  - Benenson, Rodrigo
A1  - Omran, Mohamed
A1  - Hosang, Jan
A1  - Schiele, Bernt
SP  - 973
EP  - 986
VL  - 40
IS  - 4
N2  - 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 backgroundversus- 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0162-8828
UR  - http://dx.doi.org/10.1109/TPAMI.2017.2700460
ID  - ref1
ER  -