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
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:
The ones who makes this happen
P.E., F.ASCE Trustee Chair
Research Associate Professor
Research Assistant Professor
Computer Vision Research Engineer
Accuracy
Expected Average Overlap(EAO)
Robustness
What we’ve done for safety