Byeongjoon Noh

Orcid: 0000-0002-0458-1573

According to our database1, Byeongjoon Noh authored at least 12 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
A novel approach for reliable pedestrian trajectory collection with behavior-based trajectory reconstruction for urban surveillance systems.
Adv. Eng. Softw., 2024

2023
Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment.
Sensors, September, 2023

2022
Vision-Based Pedestrian's Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System.
Sensors, 2022

Identifying the exterior image of buildings on a 3D map and extracting elevation information using deep learning and digital image processing.
CoRR, 2022

Deep Learning-based Approach on Risk Estimation of Urban Traffic Accidents.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

Asymmetric Long-Term Graph Multi-Attention Network for Traffic Speed Prediction.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

2021
Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model.
CoRR, 2021

Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor.
CoRR, 2021

Vision based Pedestrian Potential Risk Analysis based on Automated Behavior Feature Extraction for Smart and Safe City.
CoRR, 2021

A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure.
CoRR, 2021

2018
Vision-based Overhead Front Point Recognition of Vehicles for Traffic Safety Analysis.
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018

2017
Movement Classification based on Acceleration Spectrogram with Dynamic Time Warping Method.
Proceedings of the 18th IEEE International Conference on Mobile Data Management, 2017


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