Toward Training-Free Automatic Detection of Freeway Incidents

2012 ◽  
Vol 2278 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Manoel M. Castro-Neto ◽  
Lee D. Han ◽  
Young-Seon Jeong ◽  
Myong K. Jeong
Keyword(s):  
Automatic Detection ◽  
Freeway Incidents

The automatic detection of unattended social category information from unfamiliar faces

2014 ◽  
Author(s):  
Douglas Martin ◽  
Rachel Swainson ◽  
Gillian Slessor ◽  
Jacqui Hutchison ◽  
Diana Marosi
Keyword(s):  
Automatic Detection ◽  
Social Category ◽  
Category Information ◽  
Unfamiliar Faces

Manual versus automatic detection of aortic annulus plane in CT scan for T-AVI screening

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
A Van Linden ◽  
J Kempfert ◽  
J Blumenstein ◽  
H Möllmann ◽  
WK Kim ◽  
...  
Keyword(s):  
Ct Scan ◽  
Automatic Detection ◽  
Aortic Annulus

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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