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Original scientific paper
https://doi.org/10.17559/TV-20171128220125

A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method

Yasin Sönmez ; Dicle University, Technical Sciences Vocational School, 21280 Diyarbakır, Turkey
Hüseyin Kutlu   ORCID icon orcid.org/0000-0003-0091-9984 ; Adıyaman University, Besni Vocational School, 02040 Adıyaman, Turkey
Engin Avci ; Fırat University, Technology Faculty - Software Engineering, 23119 Elazığ, Turkey

Fulltext: english, pdf (1 MB) pages 107-113 downloads: 314* cite
APA 6th Edition
Sönmez, Y., Kutlu, H. & Avci, E. (2019). A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method. Tehnički vjesnik, 26 (1), 107-113. https://doi.org/10.17559/TV-20171128220125
MLA 8th Edition
Sönmez, Yasin, et al. "A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method." Tehnički vjesnik, vol. 26, no. 1, 2019, pp. 107-113. https://doi.org/10.17559/TV-20171128220125. Accessed 10 Apr. 2020.
Chicago 17th Edition
Sönmez, Yasin, Hüseyin Kutlu and Engin Avci. "A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method." Tehnički vjesnik 26, no. 1 (2019): 107-113. https://doi.org/10.17559/TV-20171128220125
Harvard
Sönmez, Y., Kutlu, H., and Avci, E. (2019). 'A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method', Tehnički vjesnik, 26(1), pp. 107-113. https://doi.org/10.17559/TV-20171128220125
Vancouver
Sönmez Y, Kutlu H, Avci E. A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method. Tehnički vjesnik [Internet]. 2019 [cited 2020 April 10];26(1):107-113. https://doi.org/10.17559/TV-20171128220125
IEEE
Y. Sönmez, H. Kutlu and E. Avci, "A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method", Tehnički vjesnik, vol.26, no. 1, pp. 107-113, 2019. [Online]. https://doi.org/10.17559/TV-20171128220125

Abstracts
The objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made.

Keywords
anomaly classification and prediction (regression); artificial learning; Extreme Learning Machine (ELM); traffic flow video analysis

Hrčak ID: 217094

URI
https://hrcak.srce.hr/217094

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