A Rapid Method Based on Vehicle Video for Multiobjects Detection
An efficient and rapid method for car detection in video is presented in this paper. In this method, rear side view of cars is used in the detection phase. And in combination with histograms of oriented gradients (HOG) which is one of the most discriminative features, a linear support vector machine...
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2013-01-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1155/2013/546752 |
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doaj-54783383f52840368d9b2ed7f6a451d42020-11-25T03:34:12ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322013-01-01510.1155/2013/54675210.1155_2013/546752A Rapid Method Based on Vehicle Video for Multiobjects DetectionQing Tian0Long Zhang1Yun Wei2Wei-wei Fei3Wen-hua Zhao4 College of Information Engineering, North China University of Technology, Beijing 100144, China College of Information Engineering, North China University of Technology, Beijing 100144, China Beijing Urban Engineering Design and Research Institute, Beijing 100037, China Systems Engineering Research Institute, CSSC, Beijing 100094, China Beijing Urban Engineering Design and Research Institute, Beijing 100037, ChinaAn efficient and rapid method for car detection in video is presented in this paper. In this method, rear side view of cars is used in the detection phase. And in combination with histograms of oriented gradients (HOG) which is one of the most discriminative features, a linear support vector machine (SVM) is used for object classification. Besides, in order to avoid car missing, Kalman filter is used to track the objects. It is known that the calculation of HOG is complex and costs the most run time. So the processing time in this method is decreased by using information of objects' areas from the previous frames. It is shown by the experimental results that the detection rate can reach 96.20% and is more accurate when choosing the fit interval number such as 5. It is also illustrated that this method can decrease the calculating time on a large degree when the accuracy is about 94.90% by comparing with traditional method of HOG combining with SVM.https://doi.org/10.1155/2013/546752 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qing Tian Long Zhang Yun Wei Wei-wei Fei Wen-hua Zhao |
spellingShingle |
Qing Tian Long Zhang Yun Wei Wei-wei Fei Wen-hua Zhao A Rapid Method Based on Vehicle Video for Multiobjects Detection Advances in Mechanical Engineering |
author_facet |
Qing Tian Long Zhang Yun Wei Wei-wei Fei Wen-hua Zhao |
author_sort |
Qing Tian |
title |
A Rapid Method Based on Vehicle Video for Multiobjects Detection |
title_short |
A Rapid Method Based on Vehicle Video for Multiobjects Detection |
title_full |
A Rapid Method Based on Vehicle Video for Multiobjects Detection |
title_fullStr |
A Rapid Method Based on Vehicle Video for Multiobjects Detection |
title_full_unstemmed |
A Rapid Method Based on Vehicle Video for Multiobjects Detection |
title_sort |
rapid method based on vehicle video for multiobjects detection |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8132 |
publishDate |
2013-01-01 |
description |
An efficient and rapid method for car detection in video is presented in this paper. In this method, rear side view of cars is used in the detection phase. And in combination with histograms of oriented gradients (HOG) which is one of the most discriminative features, a linear support vector machine (SVM) is used for object classification. Besides, in order to avoid car missing, Kalman filter is used to track the objects. It is known that the calculation of HOG is complex and costs the most run time. So the processing time in this method is decreased by using information of objects' areas from the previous frames. It is shown by the experimental results that the detection rate can reach 96.20% and is more accurate when choosing the fit interval number such as 5. It is also illustrated that this method can decrease the calculating time on a large degree when the accuracy is about 94.90% by comparing with traditional method of HOG combining with SVM. |
url |
https://doi.org/10.1155/2013/546752 |
work_keys_str_mv |
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