Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification
Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce er...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2020-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1722_9749fae8e64658949e4fc6907940b5f7.pdf |
| _version_ | 1819299424575160320 |
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| author | R. Asgarian Dehkordi H. Khosravi |
| author_facet | R. Asgarian Dehkordi H. Khosravi |
| author_sort | R. Asgarian Dehkordi |
| collection | DOAJ |
| description | Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous results. This paper presents a methodology that uses two criteria to identify common vehicle types. The first criterion is feature extraction and classification and the second criterion is to use the dimensions of car for classification. This method consists of three phases. In the first phase, the coordinates of the vanishing points are obtained. In the second phase, the bounding box and dimensions are calculated for each passing vehicle. Finally, in the third phase, the exact vehicle type is determined by combining the results of the first and second criteria. To evaluate the proposed method, a dataset of images and videos, prepared by the authors, has been used. This dataset is recorded from places similar to those of a roadside camera. Most existing methods use high-quality images for evaluation and are not applicable in the real world, but in the proposed method real-world video frames are used to determine the exact type of vehicle, and the accuracy of 89.5% is achieved, which represents a good performance. |
| first_indexed | 2024-12-24T05:45:35Z |
| format | Article |
| id | doaj.art-095b080894bf4b98b4d7ba234ff5d62e |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-24T05:45:35Z |
| publishDate | 2020-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-095b080894bf4b98b4d7ba234ff5d62e2022-12-21T17:12:36ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442020-07-018342743810.22044/jadm.2020.8375.19751722Vehicle Type Recognition based on Dimension Estimation and Bag of Word ClassificationR. Asgarian Dehkordi0H. Khosravi1Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous results. This paper presents a methodology that uses two criteria to identify common vehicle types. The first criterion is feature extraction and classification and the second criterion is to use the dimensions of car for classification. This method consists of three phases. In the first phase, the coordinates of the vanishing points are obtained. In the second phase, the bounding box and dimensions are calculated for each passing vehicle. Finally, in the third phase, the exact vehicle type is determined by combining the results of the first and second criteria. To evaluate the proposed method, a dataset of images and videos, prepared by the authors, has been used. This dataset is recorded from places similar to those of a roadside camera. Most existing methods use high-quality images for evaluation and are not applicable in the real world, but in the proposed method real-world video frames are used to determine the exact type of vehicle, and the accuracy of 89.5% is achieved, which represents a good performance.http://jad.shahroodut.ac.ir/article_1722_9749fae8e64658949e4fc6907940b5f7.pdfbag of wordscamera calibrationdimension estimationvehicle type recognition |
| spellingShingle | R. Asgarian Dehkordi H. Khosravi Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification Journal of Artificial Intelligence and Data Mining bag of words camera calibration dimension estimation vehicle type recognition |
| title | Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification |
| title_full | Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification |
| title_fullStr | Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification |
| title_full_unstemmed | Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification |
| title_short | Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification |
| title_sort | vehicle type recognition based on dimension estimation and bag of word classification |
| topic | bag of words camera calibration dimension estimation vehicle type recognition |
| url | http://jad.shahroodut.ac.ir/article_1722_9749fae8e64658949e4fc6907940b5f7.pdf |
| work_keys_str_mv | AT rasgariandehkordi vehicletyperecognitionbasedondimensionestimationandbagofwordclassification AT hkhosravi vehicletyperecognitionbasedondimensionestimationandbagofwordclassification |