TY - GEN
T1 - Boosting CNN-based pedestrian detection via 3d lidar fusion in autonomous driving
AU - Dou, Jian
AU - Fang, Jianwu
AU - Li, Tao
AU - Xue, Jianru
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Robust pedestrian detection has been treated as one of the main pursuits for excellent autonomous driving. Recently, some convolutional neural networks (CNN) based detectors have made large progress for this goal, such as Faster R-CNN. However, the performance of them still needs a large space to be boosted, even owning the complex learning architectures. In this paper, we novelly introduce the 3D LiDAR sensor to boost the CNN-based pedestrian detection. Facing the heterogeneous and asynchronous properties of two different sensors, we firstly introduce an accurate calibration method for visual and LiDAR sensors. Then, some physically geometrical clues acquired by 3D LiDAR are explored to eliminate the erroneous pedestrian proposals generated by the state-of-the-art CNN-based detectors. Exhaustive experiments verified the superiority of the proposed method.
AB - Robust pedestrian detection has been treated as one of the main pursuits for excellent autonomous driving. Recently, some convolutional neural networks (CNN) based detectors have made large progress for this goal, such as Faster R-CNN. However, the performance of them still needs a large space to be boosted, even owning the complex learning architectures. In this paper, we novelly introduce the 3D LiDAR sensor to boost the CNN-based pedestrian detection. Facing the heterogeneous and asynchronous properties of two different sensors, we firstly introduce an accurate calibration method for visual and LiDAR sensors. Then, some physically geometrical clues acquired by 3D LiDAR are explored to eliminate the erroneous pedestrian proposals generated by the state-of-the-art CNN-based detectors. Exhaustive experiments verified the superiority of the proposed method.
UR - https://www.scopus.com/pages/publications/85041839718
U2 - 10.1007/978-3-319-71589-6_1
DO - 10.1007/978-3-319-71589-6_1
M3 - 会议稿件
AN - SCOPUS:85041839718
SN - 9783319715889
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 13
BT - Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers
A2 - Kong, Xiangwei
A2 - Zhao, Yao
A2 - Taubman, David
PB - Springer Verlag
T2 - 9th International Conference on Image and Graphics, ICIG 2017
Y2 - 13 September 2017 through 15 September 2017
ER -