A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory
The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/6/1372 |
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| author | Ziqi Dong Furong Tian Hua Yang Tao Sun Wenchuan Zhang Dan Ruan |
| author_facet | Ziqi Dong Furong Tian Hua Yang Tao Sun Wenchuan Zhang Dan Ruan |
| author_sort | Ziqi Dong |
| collection | DOAJ |
| description | The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame. |
| first_indexed | 2024-03-11T06:39:26Z |
| format | Article |
| id | doaj.art-f73b959232d442ca8bee6df8d2ff4e87 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:39:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f73b959232d442ca8bee6df8d2ff4e872023-11-17T10:44:35ZengMDPI AGElectronics2079-92922023-03-01126137210.3390/electronics12061372A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone TrajectoryZiqi Dong0Furong Tian1Hua Yang2Tao Sun3Wenchuan Zhang4Dan Ruan5School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, ChinaDepartment of Radiation Oncology, University of California, Los Angeles, CA 90095, USAThe advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame.https://www.mdpi.com/2079-9292/12/6/1372trajectory reconstructiontrajectory matchingtrajectory feature engineeringpedestrian trackingsuspect tracking |
| spellingShingle | Ziqi Dong Furong Tian Hua Yang Tao Sun Wenchuan Zhang Dan Ruan A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory Electronics trajectory reconstruction trajectory matching trajectory feature engineering pedestrian tracking suspect tracking |
| title | A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory |
| title_full | A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory |
| title_fullStr | A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory |
| title_full_unstemmed | A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory |
| title_short | A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory |
| title_sort | framework with elaborate feature engineering for matching face trajectory and mobile phone trajectory |
| topic | trajectory reconstruction trajectory matching trajectory feature engineering pedestrian tracking suspect tracking |
| url | https://www.mdpi.com/2079-9292/12/6/1372 |
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