Optimized frame detection technique in vehicle accident using deep learning
Joint Authors
Anwar, Mardin A.
Ali, Abbas M.
Sharif, Sharif M.
Source
ZANCO Journal of Pure and Applied Sciences
Issue
Vol. 32, Issue 4 (31 Aug. 2020), pp.38-47, 10 p.
Publisher
Salahaddin University-Erbil Department of Scientific Publications
Publication Date
2020-08-31
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Video processing becomes one of the most popular and needed steps in machine leering.
Todays, Cameras are installed in many places for many reasons including government services.
One of the most applications for this concern is traffic police services.
One of the main problems of using videos in machine learning application is the duration of the video; which is consuming time, paperwork and space in processing.
This leads to increase the computation cost through a high number of frames.
This paper proposes an algorithm to optimize videos duration using a Gaussian mixture model (GMM) method for real accident video.
The Histogram of Gradient (HoG) has been used to extract the features of the video frames, a scratch CNN has been designed and conducted on two common datasets; Stanford Dogs Dataset (SDD) and Vehicle Make and Model Recognition Dataset (VMMRdb) in addition to a local dataset that created for this research.
The experimental work is done in two ways, the first is after applying GMM, the finding revealed that the number of frames in the dataset was decreased by nearly 51%.
The second is comparing the accuracy and complexity of these datasets has been done.
Whereas the experimental results of accuracy illustrated for the proposed CNN, 85% on the local dataset, 85% on SDD Dataset and 86% on VMMRdb Dataset.
However, applying GoogleNet and AlexNet on the same datasets achieved 82%, 79%, 80%, 83%, 81%, 83% respectively.
American Psychological Association (APA)
Anwar, Mardin A.& Sharif, Sharif M.& Ali, Abbas M.. 2020. Optimized frame detection technique in vehicle accident using deep learning. ZANCO Journal of Pure and Applied Sciences،Vol. 32, no. 4, pp.38-47.
https://search.emarefa.net/detail/BIM-1386735
Modern Language Association (MLA)
Anwar, Mardin A.…[et al.]. Optimized frame detection technique in vehicle accident using deep learning. ZANCO Journal of Pure and Applied Sciences Vol. 32, no. 4 (2020), pp.38-47.
https://search.emarefa.net/detail/BIM-1386735
American Medical Association (AMA)
Anwar, Mardin A.& Sharif, Sharif M.& Ali, Abbas M.. Optimized frame detection technique in vehicle accident using deep learning. ZANCO Journal of Pure and Applied Sciences. 2020. Vol. 32, no. 4, pp.38-47.
https://search.emarefa.net/detail/BIM-1386735
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 46-47
Record ID
BIM-1386735