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Found 21 papers, 3 papers with code
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and accuracy of these systems.
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video.
3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data
These are the need for multiple LiDAR systems to obtain complete point cloud information of objects of interest, as well as the labor-intensive process of annotating 3D bounding boxes for object detection tasks.
Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images
The Pavement Condition Index (PCI) is a widely used metric for evaluating pavement performance based on the type, extent and severity of distresses detected on a pavement surface.
Edge Computing-Enabled Road Condition Monitoring: System Development and Evaluation
Overall, our proposed device demonstrates significant potential in providing real-time pavement condition data to State Highway Agencies (SHA) and Department of Transportation (DOTs) with a satisfactory level of accuracy.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
GazeSAM: What You See is What You Segment
This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation.
Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted.
Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5
The results demonstrate impressive precision, recall, and mAP scores of 0. 848, 0. 599, and 0. 641, respectively for the training data.
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads.
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8
Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time.
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos
Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes.
AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment
Furthermore, most studies are restricted to modeling the ego vehicle's acceleration, which is insufficient to explain the behavior of the ego vehicle.
GC-GRU-N for Traffic Prediction using Loop Detector Data
Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task.
Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification
Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data is continuous.
The 1st Data Science for Pavements Challenge
The Data Science for Pavement Challenge (DSPC) seeks to accelerate the research and development of automated vision systems for pavement condition monitoring and evaluation by providing a platform with benchmarked datasets and codes for teams to innovate and develop machine learning algorithms that are practice-ready for use by industry.
A Region-Based Deep Learning Approach to Automated Retail Checkout
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.
Comparative Analysis of Machine Learning Models for Predicting Travel Time
The study concluded that the ARIMA model was the best model architecture for travel time prediction and forecasting.
Identifying the Factors that Influence Urban Public Transit Demand
Common factors that can influence urban public transit demand can be internal and/or external factors.
Mobile Sensing for Multipurpose Applications in Transportation
Routine and consistent data collection is required to address contemporary transportation issues. The cost of data collection increases significantly when sophisticated machines are used to collect data.
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
Our approach included creating a detection model, followed by anomaly detection and analysis.
