Abnormality detection using K-means data stream clustering algorithm in intelligent surveillance system
Other Title(s)
تمييز الحالات غير الطبيعية باستخدام خوارزمية (K-means data stream clustering) في أنظمة المراقبة الذكية
Joint Authors
Shati, Narjis Mazal
Karim, Abd al-Amir Abd Allah
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 9, Issue 1 (30 Jun. 2017), pp.82-98, 17 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2017-06-30
Country of Publication
Iraq
No. of Pages
17
Main Subjects
Mathematics
Information Technology and Computer Science
Abstract EN
In this research work a k-Means clustering technique utilized in a new data stream clustering method used in abnormal detection system.
This system implies the use of a set of features (such as: distance, direction, x-coordinate, y-coordinate) extracted from set of pairs of interest point that obtained using HARRIS or FAST detector from the frames of video clips in two publically available datasets, the first UCSD pedestrian dataset (ped1 and ped2 datasets), and the second VIRAT video dataset.
The results indicated that using HARRIS detector achieved detection rate 1% with 6% false alarms by using UCSD (Ped1) dataset, 10.75 % detection Rate with 10 % false alarm rate by using UCSD (Ped2) dataset, and 5% detection rate with 40% false alarms by using VIRAT dataset.
While for FAST detector, the achieved detection rates are 0.5 %, 10.75 %, and 4.08 % while the false alarm rates are 5%, 10.50%, and 45.92% by using UCSD (Ped1), UCSD (Ped2), and VIRAT datasets respectively.
American Psychological Association (APA)
Karim, Abd al-Amir Abd Allah& Shati, Narjis Mazal. 2017. Abnormality detection using K-means data stream clustering algorithm in intelligent surveillance system. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 9, no. 1, pp.82-98.
https://search.emarefa.net/detail/BIM-787372
Modern Language Association (MLA)
Karim, Abd al-Amir Abd Allah& Shati, Narjis Mazal. Abnormality detection using K-means data stream clustering algorithm in intelligent surveillance system. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 9, no. 1 (2017), pp.82-98.
https://search.emarefa.net/detail/BIM-787372
American Medical Association (AMA)
Karim, Abd al-Amir Abd Allah& Shati, Narjis Mazal. Abnormality detection using K-means data stream clustering algorithm in intelligent surveillance system. al-Qadisiyah Journal for Computer Science and Mathematics. 2017. Vol. 9, no. 1, pp.82-98.
https://search.emarefa.net/detail/BIM-787372
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 97
Record ID
BIM-787372