1 citations · 1 across the 3 of their papers we have counts for
6 papers
Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model
Byeongjoon Noh, Hansaem Park, Hwasoo Yeo
Traffic accidents are a threat to human lives, particularly pedestrians causing premature deaths. Therefore, it is necessary to devise systems to prevent accidents in advance and r…
Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor
Byeongjoon Noh, Dongho Ka, Wonjun Noh +1
Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks, for a variety of reasons. To date, an extensive variety of studies have r…
Vision based Pedestrian Potential Risk Analysis based on Automated Behavior Feature Extraction for Smart and Safe City
Byeongjoon Noh, Dongho Ka, David Lee +1
Despite recent advances in vehicle safety technologies, road traffic accidents still pose a severe threat to human lives and have become a leading cause of premature deaths. In par…
A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure
Byeongjoon Noh, Hwasoo Yeo
Road traffic accidents, especially vehicle pedestrian collisions in crosswalk, globally pose a severe threat to human lives and have become a leading cause of premature deaths. In…
Traffic Data Imputation using Deep Convolutional Neural Networks
Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo +2
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, w…
Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction
Seongjin Choi, Jiwon Kim, Hwasoo Yeo
With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as t…