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Real-Time Accident Detection in Traffic Surveillance Using Deep Learning
Hadi Ghahremannezhad
, Hang Shi
,
Chengjun Liu
Computer Science
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
58
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Scopus citations
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Dive into the research topics of 'Real-Time Accident Detection in Traffic Surveillance Using Deep Learning'. Together they form a unique fingerprint.
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Computer Science
Computer Vision
100%
Traffic Monitoring
100%
Monitoring System
100%
Deep Learning
100%
Experimental Result
100%
Surveillance Camera
100%
Real-Time Application
100%
Automatic Detection
100%
False Alarm Rate
100%
Video Sequences
100%
Kalman Filter
100%
Object Detection
100%
Detection Rate
100%
Illumination Condition
100%
YouTube
100%
Hungarian Algorithm
100%
Tracking Object
100%
Traffic Management System
100%
Based Object Tracking
100%
Keyphrases
Deep Learning
100%
Traffic Surveillance
100%
Accident Detection
100%
Vehicle-to-vehicle
66%
Urban Intersections
66%
Object Tracking
66%
Trajectory Conflict
66%
Publicly Available
33%
Cost Function
33%
Traffic Monitoring System
33%
Video Data
33%
Surveillance Applications
33%
Shape Change
33%
Surveillance Camera
33%
Time Application
33%
Automatic Detection
33%
Low False Alarm Rate
33%
Traffic Management System
33%
Video Sequences
33%
Kalman Filter
33%
Object Detection
33%
Real-time Traffic
33%
Illumination Conditions
33%
Occlusion
33%
Object Trajectory
33%
High Detection Rate
33%
Traffic Accidents
33%
YouTube
33%
Efficient Framework
33%
Traffic Video
33%
Hungarian Algorithm
33%
Overlapping Objects
33%
Computer Vision Techniques
33%
Conflict Analysis
33%
YOLOv4
33%
Automatic Accident Detection
33%
Near Accident
33%
Object Association
33%
Tory
33%
Vehicle-to-pedestrian
33%