Near Miss Event Detection System
N.M.E.D.S
Grid- based spatial autocorrelation analytics
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 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 to identify dangerous locations
- Predict road users’ intention (e.g., pedestrian crossing)
- Predict road safety condition using surrogate safety measures
System Demo
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Hours Testing
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Sample Size
Our Performance
OUR Example
What we’ve done for safety
