Dense Traffic Detection at Highway-Railroad Grade Crossings-Reference-Cited by-同舟云学术

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Dense Traffic Detection at Highway-Railroad Grade Crossings

Published:2022-09 Issue:9 Volume:23 Page:15498-15511
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Guo Feng¹,Jiang Zhuocheng²,Wang Yi²^ORCID,Chen Chen³^ORCID,Qian Yu¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA

2. Department of Mechanical Engineering, University of South Carolina, Columbia, SC, USA

3. Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA

Funder

Federal Railroad Administration

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9893028/09686603.pdf?arnumber=9686603

Reference45 articles.

1. Rethinking transformer-based set prediction for object detection;sun;arXiv 2011 10881,2020

2. PSANet: Point-wise Spatial Attention Network for Scene Parsing

3. CODAN: Counting-driven Attention Network for Vehicle Detection in Congested Scenes

4. Kernel-based density map generation for dense object counting;wan;IEEE Trans Pattern Anal Mach Intell,2020

5. Extremely Overlapping Vehicle Counting

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3. Semantic Communication-Empowered Vehicle Count Prediction for Traffic Management;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

4. Intelligent railroad inspection and monitoring;Frontiers in Built Environment;2024-04-12

5. Merging planning in dense traffic scenarios using interactive safe reinforcement learning;Knowledge-Based Systems;2024-04

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