Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision

Stereo Vision ◽

Traffic Sign Detection and 3D Localization via Deep Convolutional Neural Networks and Stereo Vision

2019 IEEE Intelligent Transportation Systems Conference (ITSC) ◽

10.1109/itsc.2019.8916958 ◽

2019 ◽

Author(s):

Gabriel Noya Doval ◽

Abdulla Al-Kaff ◽

Jorge Beltran ◽

Fernando Garcia Fernandez ◽

Gerardo Fernandez Lopez

Keyword(s):

Neural Networks ◽

Stereo Vision ◽

Deep Convolutional Neural Networks ◽

Traffic Sign ◽

3D Localization ◽

Sign Detection ◽

Traffic Sign Detection

Fusing LIDAR and images for pedestrian detection using convolutional neural networks

2016 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2016.7487370 ◽

2016 ◽

Cited By ~ 34

Author(s):

Joel Schlosser ◽

Christopher K. Chow ◽

Zsolt Kira

Keyword(s):

Neural Networks ◽

Nighttime Pedestrian Detection Based on Thermal Imaging and Convolutional Neural Networks

Sensors and Materials ◽

10.18494/sam.2020.2838 ◽

2020 ◽

Vol 32 (10) ◽

pp. 3157

Author(s):

Yung-Yao Chen ◽

Guan-Yi Li ◽

Sin-Ye Jhong ◽

Ping-Han Chen ◽

Chiung-Cheng Tsai ◽

...

Keyword(s):

Neural Networks ◽

Thermal Imaging ◽

Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment

IEEE Access ◽

10.1109/access.2019.2899105 ◽

2019 ◽

Vol 7 ◽

pp. 23027-23037 ◽

Cited By ~ 4

Author(s):

Inyong Yun ◽

Cheolkon Jung ◽

Xinran Wang ◽

Alfred O. Hero ◽

Joong Kyu Kim

Keyword(s):

Neural Networks ◽

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks

10.1109/cvprw.2017.62 ◽

2017 ◽

Cited By ~ 6

Author(s):

Rytis Verbickas ◽

Robert Laganiere ◽

Daniel Laroche ◽

Changyun Zhu ◽

Xiaoyin Xu ◽

...

Keyword(s):

Neural Networks ◽

Low Power ◽

Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks

2006 IEEE Intelligent Vehicles Symposium ◽

10.1109/ivs.2006.1689630 ◽

2006 ◽

Cited By ~ 46

Author(s):

M. Szarvas ◽

U. Sakai ◽

Jun Ogata

Keyword(s):

Neural Networks ◽

Real Time ◽

Two Parallel Deep Convolutional Neural Networks for pedestrian detection

2015 International Conference on Image and Vision Computing New Zealand (IVCNZ) ◽

10.1109/ivcnz.2015.7761510 ◽

2015 ◽

Cited By ~ 1

Author(s):

Bo-Yao Lin ◽

Chu-Song Chen

Keyword(s):

Neural Networks ◽

Deep Convolutional Neural Networks

Pedestrian Detection ◽

Deep convolutional neural networks for pedestrian detection with skip pooling

2017 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2017.7966103 ◽

2017 ◽

Cited By ~ 6

Author(s):

Jie Liu ◽

Xingkun Gao ◽

Nianyuan Bao ◽

Jie Tang ◽

Gangshan Wu

Keyword(s):

Neural Networks ◽

Deep Convolutional Neural Networks

Pedestrian Detection ◽

Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection

2017 IEEE Winter Conference on Applications of Computer Vision (WACV) ◽

10.1109/wacv.2017.66 ◽

2017 ◽

Cited By ~ 2

Author(s):

Chi-Hao Wu ◽

Weihao Gan ◽

De Lan ◽

C.-C. Jay Kuo

Keyword(s):

Neural Networks ◽

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) ◽

HOG and Gabor Filter Based Pedestrian Detection using Convolutional Neural Networks

10.1109/ecace.2019.8679133 ◽

2019 ◽

Author(s):

Fahim Ahmed ◽

Badhon Ahmed Topu ◽

S.M. Mohidul Islam

Keyword(s):

Neural Networks ◽

Gabor Filter ◽