Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach
Moving traces are essential data for target detection and associated behavior recognition. Previous studies have used time–location sequences, route maps, or tracking videos to establish mathematical recognition models for behavior recognition. The multimodal approach has seldom been considered beca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/2/367 |
| _version_ | 1797344226751545344 |
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| author | Meng Zhang Lingxi Zhang Tao Liu |
| author_facet | Meng Zhang Lingxi Zhang Tao Liu |
| author_sort | Meng Zhang |
| collection | DOAJ |
| description | Moving traces are essential data for target detection and associated behavior recognition. Previous studies have used time–location sequences, route maps, or tracking videos to establish mathematical recognition models for behavior recognition. The multimodal approach has seldom been considered because of the limited modality of sensing data. With the rapid development of natural language processing and computer vision, the multimodal model has become a possible choice to process multisource data. In this study, we have proposed a mathematical model for aircraft behavior recognition with joint data manners. The feature abstraction, cross-modal fusion, and classification layers are included in the proposed model for obtaining multiscale features and analyzing multimanner information. Attention has been placed on providing self- and cross-relation assessments on the spatiotemporal and geographic data related to a moving object. We have adopted both a feedforward network and a softmax function to form the classifier. Moreover, we have enabled a modality-increasing phase, combining longitude and latitude sequences with related geographic maps to avoid monotonous data. We have collected an aircraft trajectory dataset of longitude and latitude sequences for experimental validation. We have demonstrated the excellent behavior recognition performance of the proposed model joint with the modality-increasing phase. As a result, our proposed methodology reached the highest accuracy of 95.8% among all the adopted methods, demonstrating the effectiveness and feasibility of trajectory-based behavior recognition. |
| first_indexed | 2024-03-08T10:59:18Z |
| format | Article |
| id | doaj.art-88b2e55f46ae4e368be333db6294d37f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T10:59:18Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-88b2e55f46ae4e368be333db6294d37f2024-01-26T16:14:11ZengMDPI AGElectronics2079-92922024-01-0113236710.3390/electronics13020367Aircraft Behavior Recognition on Trajectory Data with a Multimodal ApproachMeng Zhang0Lingxi Zhang1Tao Liu2Southwest China Institute of Electronic Technology, Chengdu 610036, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, ChinaMoving traces are essential data for target detection and associated behavior recognition. Previous studies have used time–location sequences, route maps, or tracking videos to establish mathematical recognition models for behavior recognition. The multimodal approach has seldom been considered because of the limited modality of sensing data. With the rapid development of natural language processing and computer vision, the multimodal model has become a possible choice to process multisource data. In this study, we have proposed a mathematical model for aircraft behavior recognition with joint data manners. The feature abstraction, cross-modal fusion, and classification layers are included in the proposed model for obtaining multiscale features and analyzing multimanner information. Attention has been placed on providing self- and cross-relation assessments on the spatiotemporal and geographic data related to a moving object. We have adopted both a feedforward network and a softmax function to form the classifier. Moreover, we have enabled a modality-increasing phase, combining longitude and latitude sequences with related geographic maps to avoid monotonous data. We have collected an aircraft trajectory dataset of longitude and latitude sequences for experimental validation. We have demonstrated the excellent behavior recognition performance of the proposed model joint with the modality-increasing phase. As a result, our proposed methodology reached the highest accuracy of 95.8% among all the adopted methods, demonstrating the effectiveness and feasibility of trajectory-based behavior recognition.https://www.mdpi.com/2079-9292/13/2/367aircraft behavior recognitiontrajectory recognitionmultimodal modelmodality increasingdata fusion |
| spellingShingle | Meng Zhang Lingxi Zhang Tao Liu Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach Electronics aircraft behavior recognition trajectory recognition multimodal model modality increasing data fusion |
| title | Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach |
| title_full | Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach |
| title_fullStr | Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach |
| title_full_unstemmed | Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach |
| title_short | Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach |
| title_sort | aircraft behavior recognition on trajectory data with a multimodal approach |
| topic | aircraft behavior recognition trajectory recognition multimodal model modality increasing data fusion |
| url | https://www.mdpi.com/2079-9292/13/2/367 |
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