Extending the Detection Range for Low-Channel Roadside LiDAR by Static Background Construction
Author:
Affiliation:
1. Department of Traffic Information and Control Engineering, Jilin University, Changchun, China
2. Texas A&M Transportation Institute, Texas A&M University, College Station, TX, USA
Funder
Graduate Innovation Fund of Jilin University
Scientific Research Project of the Education Department of Jilin Province
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09723076.pdf?arnumber=9723076
Reference42 articles.
1. Vehicle Tracking and Speed Estimation From Roadside Lidar
2. An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data
3. Deep learning for occluded and multi‐scale pedestrian detection: A review
4. Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection
5. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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