Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM
Traffic flow prediction is important for urban planning and traffic congestion alleviation as
well as for intelligent traffic management systems. Due to the periodic characteristics and …
well as for intelligent traffic management systems. Due to the periodic characteristics and …
Convolutional neural network for recognizing highway traffic congestion
H Cui, G Yuan, N Liu, M Xu, H Song - Journal of Intelligent …, 2020 - Taylor & Francis
We investigates the performance of deep Convolutional Neural Network (CNN) for
recognizing highway traffic congestion state in surveillance camera images. Different from …
recognizing highway traffic congestion state in surveillance camera images. Different from …
Fast-Yolo-rec: Incorporating Yolo-base detection and recurrent-base prediction networks for fast vehicle detection in consecutive images
Despite significant advances and innovations in deep network-based vehicle detection
methods, finding a balance between detector accuracy and speed remains a significant …
methods, finding a balance between detector accuracy and speed remains a significant …
Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods
Transportation safety is highly correlated with driving behavior, especially human error
playing a key role in a large portion of crashes. Modern instrumentation and computational …
playing a key role in a large portion of crashes. Modern instrumentation and computational …
MobileNetV2 with Spatial Attention module for traffic congestion recognition in surveillance images
C Lin, X Hu, Y Zhan, X Hao - Expert Systems with Applications, 2024 - Elsevier
Traffic congestion recognition is essential for road traffic condition monitoring and improving
transportation operation efficiency. Recent works have proposed using computer vision to …
transportation operation efficiency. Recent works have proposed using computer vision to …
Recent trending on learning based video compression: A survey
The increase of video content and video resolution drive more exploration of video
compression techniques recently. Meanwhile, learning-based video compression is …
compression techniques recently. Meanwhile, learning-based video compression is …
A novel image-based convolutional neural network approach for traffic congestion estimation
Traditional image-based traffic congestion estimation methods generally include two steps,
which first extract the vehicles from the surveillance images, then calculate the congestion …
which first extract the vehicles from the surveillance images, then calculate the congestion …
Opitrack: a wearable-based clinical opioid use tracker with temporal convolutional attention networks
Opioid use disorder is a medical condition with major social and economic consequences.
While ubiquitous physiological sensing technologies have been widely adopted and …
While ubiquitous physiological sensing technologies have been widely adopted and …
A computer vision algorithm for locating and recognizing traffic signal control light status and countdown time
X Chen, Y Chen, G Zhang - Journal of Intelligent Transportation …, 2021 - Taylor & Francis
It is of practical importance for individual vehicles to automatically identify traffic signal light
status to facilitate their decision makings for traffic safety performance enhancement and …
status to facilitate their decision makings for traffic safety performance enhancement and …
Safe Deep Driving Behavior Detection (S3D)
E Khosravi, AMA Hemmatyar, MJ Siavoshani… - IEEE …, 2022 - ieeexplore.ieee.org
The human factor is one of the most critical parameters in car accidents and even traffic
occurrences. Driving style affected by human factors comprises driving events (maneuvers) …
occurrences. Driving style affected by human factors comprises driving events (maneuvers) …