Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916321262829568 |
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| author | Kittivorawong, Chanwut Ge, Yongming Helal, Yousef Cheung, Alvin |
| author_facet | Kittivorawong, Chanwut Ge, Yongming Helal, Yousef Cheung, Alvin |
| contents | Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such geospatial videos on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively.
In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative, build-filter-observe paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3x, while maintaining up to 97.1% accuracy compared to unoptimized execution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03276 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations Kittivorawong, Chanwut Ge, Yongming Helal, Yousef Cheung, Alvin Databases Computer Vision and Pattern Recognition Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such geospatial videos on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative, build-filter-observe paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3x, while maintaining up to 97.1% accuracy compared to unoptimized execution. |
| title | Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations |
| topic | Databases Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.03276 |