Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features
The information acquisition and automatic processing technology based on visual surveillance sensors in intelligent transportation system (ITS) has become an important application field of computer vision technology. The first step of a visual traffic surveillance system usually needs to correctly d...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8736962/ |
| _version_ | 1828736183668047872 |
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| author | Yu Wang Xiaojuan Ban Huan Wang Di Wu Hao Wang Shouqing Yang Sinuo Liu Jinhui Lai |
| author_facet | Yu Wang Xiaojuan Ban Huan Wang Di Wu Hao Wang Shouqing Yang Sinuo Liu Jinhui Lai |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | The information acquisition and automatic processing technology based on visual surveillance sensors in intelligent transportation system (ITS) has become an important application field of computer vision technology. The first step of a visual traffic surveillance system usually needs to correctly detect objects from videos and classify them into different categories. In this paper, the improved spatiotemporal sample consistency algorithm (STSC) is proposed, to enhance the robustness of background subtraction in complex scenes. To address this challenge of classifying acquired from visual traffic surveillance sensors in a particular area in China, improved spatiotemporal sample consistency algorithm is proposed, which consists of two main stages. In the first stage, the robustness of moving object detection is further provided by the method we proposed based spatiotemporal sample consistency; in the second stage, we propose the target classification method based prior knowledge, in addition correcting in tracking progress. The experiments on the CDnet 2014, MIO-TCD, and BIT-Vehicle show that the method we proposed successfully overcomes the adverse effects in the complex environment with different shooting angle and resolution taken by single fixed cameras, besides effectively reduces the false alarm rate of classification. |
| first_indexed | 2024-04-12T23:20:48Z |
| format | Article |
| id | doaj.art-b8b2a15b08214fb69762a0db190356de |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:20:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b8b2a15b08214fb69762a0db190356de2022-12-22T03:12:32ZengIEEEIEEE Access2169-35362019-01-017802878029910.1109/ACCESS.2019.29231998736962Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal FeaturesYu Wang0https://orcid.org/0000-0002-0445-3731Xiaojuan Ban1Huan Wang2Di Wu3Hao Wang4Shouqing Yang5Sinuo Liu6Jinhui Lai7Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Information Science Technology, Shijiazhuang Tiedao University, Shijiazhuang, ChinaDepartment of ICT and Natural Science, Norwegian University of Science and Technology, Ålesund, NorwayDepartment of Computer Science, Norwegian University of Science and Technology, Gjøvik, NorwayNational University of Defense Technology, Changsha, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaNational University of Defense Technology, Changsha, ChinaThe information acquisition and automatic processing technology based on visual surveillance sensors in intelligent transportation system (ITS) has become an important application field of computer vision technology. The first step of a visual traffic surveillance system usually needs to correctly detect objects from videos and classify them into different categories. In this paper, the improved spatiotemporal sample consistency algorithm (STSC) is proposed, to enhance the robustness of background subtraction in complex scenes. To address this challenge of classifying acquired from visual traffic surveillance sensors in a particular area in China, improved spatiotemporal sample consistency algorithm is proposed, which consists of two main stages. In the first stage, the robustness of moving object detection is further provided by the method we proposed based spatiotemporal sample consistency; in the second stage, we propose the target classification method based prior knowledge, in addition correcting in tracking progress. The experiments on the CDnet 2014, MIO-TCD, and BIT-Vehicle show that the method we proposed successfully overcomes the adverse effects in the complex environment with different shooting angle and resolution taken by single fixed cameras, besides effectively reduces the false alarm rate of classification.https://ieeexplore.ieee.org/document/8736962/Moving object detectionvehicle type classificationspatio-temporalmonitoring video |
| spellingShingle | Yu Wang Xiaojuan Ban Huan Wang Di Wu Hao Wang Shouqing Yang Sinuo Liu Jinhui Lai Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features IEEE Access Moving object detection vehicle type classification spatio-temporal monitoring video |
| title | Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features |
| title_full | Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features |
| title_fullStr | Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features |
| title_full_unstemmed | Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features |
| title_short | Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features |
| title_sort | detection and classification of moving vehicle from video using multiple spatio temporal features |
| topic | Moving object detection vehicle type classification spatio-temporal monitoring video |
| url | https://ieeexplore.ieee.org/document/8736962/ |
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