TY - JOUR
T1 - A unified framework for concurrent pedestrian and cyclist detection
AU - Li, Xiaofei
AU - Li, Lingxi
AU - Flohr, F.B.
AU - Wang, Jianqiang
AU - Xiong, Hui
AU - Bernhard, Morys
AU - Pan, Shuyue
AU - Gavrila, D. M.
AU - Li, Keqiang
PY - 2017
Y1 - 2017
N2 - Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.
AB - Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.
KW - upper body detection
KW - Multiple potential regions
KW - pedestrian and cyclist detection
KW - R-CNN
U2 - 10.1109/TITS.2016.2567418
DO - 10.1109/TITS.2016.2567418
M3 - Article
SN - 1524-9050
VL - 18
SP - 269
EP - 281
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 2
ER -