VQPy: An Object-Oriented Approach to Modern Video Analytics
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914821310513152 |
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| author | Yu, Shan Zhu, Zhenting Chen, Yu Xu, Hanchen Zhao, Pengzhan Wang, Yang Padmanabhan, Arthi Latapie, Hugo Xu, Harry |
| author_facet | Yu, Shan Zhu, Zhenting Chen, Yu Xu, Hanchen Zhao, Pengzhan Wang, Yang Padmanabhan, Arthi Latapie, Hugo Xu, Harry |
| contents | Video analytics is widely used in contemporary systems and services. At the forefront of video analytics are video queries that users develop to find objects of particular interest. Building upon the insight that video objects (e.g., human, animals, cars, etc.), the center of video analytics, are similar in spirit to objects modeled by traditional object-oriented languages, we propose to develop an object-oriented approach to video analytics. This approach, named VQPy, consists of a frontend$\unicode{x2015}$a Python variant with constructs that make it easy for users to express video objects and their interactions$\unicode{x2015}$as well as an extensible backend that can automatically construct and optimize pipelines based on video objects. We have implemented and open-sourced VQPy, which has been productized in Cisco as part of its DeepVision framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01623 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | VQPy: An Object-Oriented Approach to Modern Video Analytics Yu, Shan Zhu, Zhenting Chen, Yu Xu, Hanchen Zhao, Pengzhan Wang, Yang Padmanabhan, Arthi Latapie, Hugo Xu, Harry Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Video analytics is widely used in contemporary systems and services. At the forefront of video analytics are video queries that users develop to find objects of particular interest. Building upon the insight that video objects (e.g., human, animals, cars, etc.), the center of video analytics, are similar in spirit to objects modeled by traditional object-oriented languages, we propose to develop an object-oriented approach to video analytics. This approach, named VQPy, consists of a frontend$\unicode{x2015}$a Python variant with constructs that make it easy for users to express video objects and their interactions$\unicode{x2015}$as well as an extensible backend that can automatically construct and optimize pipelines based on video objects. We have implemented and open-sourced VQPy, which has been productized in Cisco as part of its DeepVision framework. |
| title | VQPy: An Object-Oriented Approach to Modern Video Analytics |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2311.01623 |