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Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes
Author(s) -
Xie Yujia,
Wang Meizhen,
Liu Xuejun,
Wang Ziran,
Mao Bo,
Wang Feiyue,
Wang Xiaozhi
Publication year - 2021
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12696
Subject(s) - computer science , computer vision , artificial intelligence , trajectory , discontinuity (linguistics) , focus (optics) , object (grammar) , image retrieval , range (aeronautics) , image (mathematics) , mathematics , mathematical analysis , physics , materials science , composite material , astronomy , optics
Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.