no code implementations • 14 Jan 2024 • Linlin Zhang, Xiang Yu, Abdulateef Daud, Abdul Rashid Mussah, Yaw Adu-Gyamfi
This study implements a three-stage video analytics framework for extracting high-resolution traffic data such vehicle counts, speed, and acceleration from infrastructure-mounted CCTV cameras.
no code implementations • 13 Jan 2024 • Linlin Zhang, Xiang Yu, Armstrong Aboah, Yaw Adu-Gyamfi
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.
no code implementations • 12 Oct 2023 • Neema Jakisa Owor, Hang Du, Abdulateef Daud, Armstrong Aboah, Yaw Adu-Gyamfi
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.
no code implementations • 9 Oct 2023 • Abdulateef Daud, Mark Amo-Boateng, Neema Jakisa Owor, Armstrong Aboah, Yaw Adu-Gyamfi
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.
no code implementations • 13 Apr 2023 • Armstrong Aboah, Bin Wang, Ulas Bagci, Yaw Adu-Gyamfi
Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time.
no code implementations • 13 Apr 2023 • Armstrong Aboah, Ulas Bagci, Abdul Rashid Mussah, Neema Jakisa Owor, Yaw Adu-Gyamfi
Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes.
no code implementations • 23 Jan 2023 • Armstrong Aboah, Abdul Rashid Mussah, Yaw Adu-Gyamfi
Furthermore, most studies are restricted to modeling the ego vehicle's acceleration, which is insufficient to explain the behavior of the ego vehicle.
no code implementations • 13 Nov 2022 • Maged Shoman, Armstrong Aboah, Abdulateef Daud, Yaw Adu-Gyamfi
Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task.
no code implementations • 10 Nov 2022 • Armstrong Aboah, Yaw Adu-Gyamfi, Senem Velipasalar Gursoy, Jennifer Merickel, Matt Rizzo, Anuj Sharma
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.
no code implementations • 10 Jun 2022 • Ashkan Behzadian, Tanner Wambui Muturi, Tianjie Zhang, Hongmin Kim, Amanda Mullins, Yang Lu, Neema Jasika Owor, Yaw Adu-Gyamfi, William Buttlar, Majidifard Hamed, Armstrong Aboah, David Mensching, Spragg Robert, Matthew Corrigan, Jack Youtchef, Dave Eshan
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.
no code implementations • 18 Apr 2022 • Maged Shoman, Armstrong Aboah, Alex Morehead, Ye Duan, Abdulateef Daud, Yaw Adu-Gyamfi
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.
no code implementations • 20 Jun 2021 • Armstrong Aboah, Michael Boeding, Yaw Adu-Gyamfi
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.
no code implementations • 14 Apr 2021 • Armstrong Aboah, Maged Shoman, Vishal Mandal, Sayedomidreza Davami, Yaw Adu-Gyamfi, Anuj Sharma
Our approach included creating a detection model, followed by anomaly detection and analysis.
1 code implementation • 21 Oct 2020 • Vishal Mandal, Abdul Rashid Mussah, Yaw Adu-Gyamfi
In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.
no code implementations • 2 Oct 2020 • Vishal Mandal, Abdul Rashid Mussah, Peng Jin, Yaw Adu-Gyamfi
Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts.
no code implementations • 31 Jul 2020 • Vishal Mandal, Yaw Adu-Gyamfi
In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI).
no code implementations • 28 Apr 2020 • Hamed Majidifard, Yaw Adu-Gyamfi, William G. Buttlar
Afterward, YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset.
no code implementations • 20 Oct 2019 • Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi, William G. Buttlar
Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community.