Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram
Smoky vehicle detection is an important task in reducing motor vehicle pollution. This paper presents a method to automatically detect smoky vehicles from the traffic surveillance videos. More specifically, the visual background extractor background subtraction algorithm and some rules are adopted t...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8481350/ |
| _version_ | 1830298688188055552 |
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| author | Huanjie Tao Xiaobo Lu |
| author_facet | Huanjie Tao Xiaobo Lu |
| author_sort | Huanjie Tao |
| collection | DOAJ |
| description | Smoky vehicle detection is an important task in reducing motor vehicle pollution. This paper presents a method to automatically detect smoky vehicles from the traffic surveillance videos. More specifically, the visual background extractor background subtraction algorithm and some rules are adopted to detect moving vehicle object and locate the key region at the back of the vehicle. Based on sufficient observations of the smoke characteristics in the real scene, three groups of features, including color moments (CMs) features, improved motion orientation histogram features, and the new model range filtering on three orthogonal planes (RF-TOP)-based features, are designed and proposed to distinguish smoky vehicles and non-smoke vehicles. The color information CM features are used as a preliminary sieve to filter out the samples that are obviously non-smoke regions. The other two groups of features are combined to one feature vector to obtain motion information and spatiotemporal information of the key region. Two strategies, including histogram and projection, are designed to extract discriminative dynamic features from the proposed model RF-TOP to characterize the key region. The pruning radial basis function neural network classifier is adopted to classify the extracted features. For the traffic surveillance videos in the daylight with sunny weather, the experimental results show that the proposed methods have better performances and work effectively with lower false alarm rates than existing methods, and the proposed method with histogram strategy achieves the best performance. |
| first_indexed | 2024-12-19T07:54:27Z |
| format | Article |
| id | doaj.art-088e569fba5b4c8fa74e686588bf1745 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:54:27Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-088e569fba5b4c8fa74e686588bf17452022-12-21T20:30:02ZengIEEEIEEE Access2169-35362018-01-016571805719010.1109/ACCESS.2018.28737578481350Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation HistogramHuanjie Tao0Xiaobo Lu1https://orcid.org/0000-0002-7707-7538School of Automation, Southeast University, Nanjing, ChinaSchool of Automation, Southeast University, Nanjing, ChinaSmoky vehicle detection is an important task in reducing motor vehicle pollution. This paper presents a method to automatically detect smoky vehicles from the traffic surveillance videos. More specifically, the visual background extractor background subtraction algorithm and some rules are adopted to detect moving vehicle object and locate the key region at the back of the vehicle. Based on sufficient observations of the smoke characteristics in the real scene, three groups of features, including color moments (CMs) features, improved motion orientation histogram features, and the new model range filtering on three orthogonal planes (RF-TOP)-based features, are designed and proposed to distinguish smoky vehicles and non-smoke vehicles. The color information CM features are used as a preliminary sieve to filter out the samples that are obviously non-smoke regions. The other two groups of features are combined to one feature vector to obtain motion information and spatiotemporal information of the key region. Two strategies, including histogram and projection, are designed to extract discriminative dynamic features from the proposed model RF-TOP to characterize the key region. The pruning radial basis function neural network classifier is adopted to classify the extracted features. For the traffic surveillance videos in the daylight with sunny weather, the experimental results show that the proposed methods have better performances and work effectively with lower false alarm rates than existing methods, and the proposed method with histogram strategy achieves the best performance.https://ieeexplore.ieee.org/document/8481350/Smoky vehicle detectioncolor momentsmotion orientation histogramrange filtering |
| spellingShingle | Huanjie Tao Xiaobo Lu Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram IEEE Access Smoky vehicle detection color moments motion orientation histogram range filtering |
| title | Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram |
| title_full | Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram |
| title_fullStr | Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram |
| title_full_unstemmed | Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram |
| title_short | Smoky Vehicle Detection Based on Range Filtering on Three Orthogonal Planes and Motion Orientation Histogram |
| title_sort | smoky vehicle detection based on range filtering on three orthogonal planes and motion orientation histogram |
| topic | Smoky vehicle detection color moments motion orientation histogram range filtering |
| url | https://ieeexplore.ieee.org/document/8481350/ |
| work_keys_str_mv | AT huanjietao smokyvehicledetectionbasedonrangefilteringonthreeorthogonalplanesandmotionorientationhistogram AT xiaobolu smokyvehicledetectionbasedonrangefilteringonthreeorthogonalplanesandmotionorientationhistogram |