Reliable and Rapid Traffic Congestion Detection Approach Based on Deep Residual Learning and Motion Trajectories

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Bibliographic Details
Published in:IEEE Access Vol. 8; pp. 182180 - 182192
Main Authors: Mohamed A. Abdelwahab, Mohamed Abdel‐Nasser, Maiya Hori
Format: Journal Article
Language:Japanese
Published: Institute of Electrical and Electronics Engineers (IEEE) 01.01.2020
Subjects:
Algorithm
Anomaly Detection in High-Dimensional Data
Artificial intelligence
Benchmark (surveying)
Biochemistry
Building and Construction
Chemistry
Computer science
Computer vision
Computer Vision and Pattern Recognition
Congestion
deep learning
Electrical engineering. Electronics. Nuclear engineering
Engineering
Gene
Geodesy
Geography
Machine learning
Motion Detection
Multiple Object Tracking
Object Tracking
Pattern recognition (psychology)
Physical Sciences
Real-time Tracking
Residual
residual network
Robustness (evolution)
TK1-9971
Traffic congestion
Traffic Flow Prediction and Forecasting
traffic surveillance system
Transport engineering
Visual Object Tracking and Person Re-identification
Visual Tracking
ISSN:2169-3536
Online Access:Get full text
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ISSN:2169-3536
DOI:10.60692/g5cc2-ve488