A Parallel-Optimized Visualization Method for Large-Scale Multiple Video-Augmented Geographic Scenes on Cesium
Bibliographic Data
| ID | 22033764 |
|---|---|
| Authors | Qingxiang Chen (0009-0002-9541-0290, Ministry of Natural Resources), Jing Chen (0000-0002-7243-0806, Wuhan University, corresponding author), Kaimin Sun (0000-0002-2664-9479, Ministry of Natural Resources), Minmin Huang (0009-0008-9114-0952, Chuzhou University), Guang Chen (0000-0002-0454-1686, Ministry of Natural Resources), Hao Liu (0000-0002-4971-7315, Ministry of Natural Resources) |
| Year | 2024 |
| Volume | 13 |
| Issue | 12 |
| Pages | 463 |
| Publication date | 2024-12-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13120463 |
| OpenAlex | W4405646732 |
| Language | EN |
| References cited | 37 |
Surveillance video has emerged as a crucial data source for web Geographic Information Systems (GIS), playing a vital role in traffic management, facility monitoring, and anti-terrorism inspections. However, previous methods encountered significant challenges in achieving effective large-scale multi-video overlapping visualization and efficiency, particularly when organizing and visualizing large-scale video-augmented geographic scenes. Therefore, we propose a parallel-optimized visualization method specifically for large-scale multi-video augmented geographic scenes on Cesium. Firstly, our method employs an improved octree-based model for the unified management of large-scale overlapping videos. Then, we introduce a novel scheduling algorithm based on Cesium, which leverages a Web Graphics Library (WebGL) parallel-optimized and dynamic Level-of-Detail (LOD) strategy. This algorithm is designed to enhance the visualization effects and efficiency of large-scale video-integrated geographic scenes. Finally, we perform comparative experiments to demonstrate that our proposed method significantly optimizes the visualization of video overlapping areas and achieves a rendering efficiency increase of up to 95%. Our method can provide a solid technical foundation for large-scale surveillance video scene management and multi-video joint monitoring
Cartography · Computer vision · Geography · Visualization · Advanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Computer Science · Video Analysis and Summarization · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |