Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City
With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional tr...
| Κύριοι συγγραφείς: | , , |
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| Μορφή: | Άρθρο |
| Γλώσσα: | English |
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MDPI AG
2023-08-01
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| Σειρά: | Sensors |
| Θέματα: | |
| Διαθέσιμο Online: | https://www.mdpi.com/1424-8220/23/15/6909 |
| _version_ | 1827730894334459904 |
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| author | Wei Wang Yujia Xie Luliang Tang |
| author_facet | Wei Wang Yujia Xie Luliang Tang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time. |
| first_indexed | 2024-03-11T00:17:01Z |
| format | Article |
| id | doaj.art-fb3b51a8a2c74e2da31d30633d5a1ab6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:01Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fb3b51a8a2c74e2da31d30633d5a1ab62023-11-18T23:36:11ZengMDPI AGSensors1424-82202023-08-012315690910.3390/s23156909Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart CityWei Wang0Yujia Xie1Luliang Tang2College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, ChinaCollege of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaWith the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.https://www.mdpi.com/1424-8220/23/15/6909intelligent transportationsmart citycomputer visionvideo GISmulti-cameravehicle trajectory |
| spellingShingle | Wei Wang Yujia Xie Luliang Tang Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City Sensors intelligent transportation smart city computer vision video GIS multi-camera vehicle trajectory |
| title | Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City |
| title_full | Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City |
| title_fullStr | Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City |
| title_full_unstemmed | Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City |
| title_short | Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City |
| title_sort | hierarchical clustering algorithm for multi camera vehicle trajectories based on spatio temporal grouping under intelligent transportation and smart city |
| topic | intelligent transportation smart city computer vision video GIS multi-camera vehicle trajectory |
| url | https://www.mdpi.com/1424-8220/23/15/6909 |
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