Journal of Signal and Information Processing

Journal of Signal and Information Processing

ISSN Print: 2159-4465
ISSN Online: 2159-4481
www.scirp.org/journal/jsip
E-mail: jsip@scirp.org
"A Statistical Framework for Real-Time Traffic Accident Recognition"
written by Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed,
published by Journal of Signal and Information Processing, Vol.1 No.1, 2010
has been cited by the following article(s):
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[1] An IoT Enabled Vehicular Decision Fusion Framework for Accident Detection and Classification.
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[2] An IoT Enabled Vehicular Decision Fusion Framework for Accident Detection and Classification
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[3] 智慧安全監控之混光 LED 路燈系統
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[4] Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings …
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[5] Traffic Accident Detection with Spatiotemporal Impact Measurement
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[6] Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings
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[7] Suspicious human activity recognition: a review
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[8] A Discriminative Framework for Action Recognition Using f-HOL Features
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[9] Inferring Traffic Incident Start Time with Loop Sensor Data
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[10] Priority-Based Classification: An Automated Traffic Approach
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[11] Shape based classification and functional forecast of traffic flow profiles
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[12] Surprise event detection of the supercomputer execution queues
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[13] AUTOMATED VEHICLE ACCIDENT TRACKING SYSTEM
National Conference on Computing Technologies Todays and Beyond, 2014
[14] An Efficient Approach to Load Balancing of Vector Maps in CyberGIS Cluster Environment
GEOMATICA, 2014
[15] Computer vision in roadway transportation systems: a survey
Journal of Electronic Imaging, 2013
[16] Complex event processing in supercomputer environment sensor and neural network based analysis
Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference on. IEEE, 2013
[17] Survey of computer vision in roadway transportation systems
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[18] Chord-Length Shape Features for Human Activity Recognition
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