ABOUT THE SYSTEM
Key features of our system
High-Resolution Analysis
High Robustness
High Accuracy
Pixel Level Video Stabilization
Car Pose Estimation
Lost and Missing Vehicles Re-matching
Semantic Segmentation
Fusion of Deep Learning and Traditional Method
System Overview
This system named “Near Miss Event Detection System (NMEDS) is developed by the UCF SST team to conduct traffic analysis using video data collected from roadside cameras. The framework of NMEDS combined the Mask-RCNN bounding box and Occlusion-Net detection algorithms to reconstruct road users' key points in a 3D view. The following are some examples of traffic analysis that could be done using the system:
- Conduct Grid-based spatial autocorrelation analytics was conducted to identify the significantly dangerous locations
- Predict road users' intention (e.g., pedestrian crossing)
- Predict road safety condition using surrogate safety measures
System Demo
Hours Testing
Sample Size
publication
Univerisity partners
THE TEAM
The ones who makes this happen
Dr. Mohamed Abdel-Aty
P.E., F.ASCE Trustee Chair
Dr. Yina Wu
Research Associate Professor
Dr. Qing Cai
Research Assistant Professor
Ou Zheng
Computer Vision Research Engineer
Our Performance.
Accuracy
Expected Average Overlap(EAO)
Robustness
OUR Example
What we've done for safety
