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

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Bibliographische Detailangaben
Veröffentlicht in:	IEEE Access Jg. 8; S. 182180 - 182192
Hauptverfasser:	Mohamed A. Abdelwahab, Mohamed Abdel‐Nasser, Maiya Hori
Format:	Journal Article
Sprache:	Japanisch
Veröffentlicht:	Institute of Electrical and Electronics Engineers (IEEE) 01.01.2020
Schlagworte:	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-Zugang:	Volltext
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