Large-scale video analytics through object-level consolidation
Visualitza/Obre
10.1007/978-3-031-06371-8_11
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/373294
Tipus de documentText en actes de congrés
Data publicació2022
EditorSpringer
Condicions d'accésAccés obert
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Abstract
As the number of installed cameras grows, so do the compute resources required to process and analyze all the images captured by these cameras. Video analytics enables new use cases, such as smart cities or autonomous driving. At the same time, it urges service providers to install additional compute resources to cope with the demand while the strict latency requirements push compute towards the end of the network, forming a geographically distributed and heterogeneous set of compute locations, shared and resource-constrained. Such landscape (shared and distributed locations) forces us to design new techniques that can optimize and distribute work among all available locations and, ideally, make compute requirements grow sublinearly with respect to the number of cameras installed. In this paper, we present FoMO (Focus on Moving Objects). This method effectively optimizes multi-camera deployments by preprocessing images for scenes, filtering the empty regions out, and composing regions of interest from multiple cameras into a single image that serves as input for a pre-trained object detection model. Results show that overall system performance can be increased by 8x while accuracy improves 40% as a by-product of the methodology, all using an off-the-shelf pre-trained model with no additional training or fine-tuning.
CitacióRivas, D. [et al.]. Large-scale video analytics through object-level consolidation. A: EAI International Convention on Science and Technologies for Smart Cities. "Science and Technologies for Smart Cities: 7th EAI International Conference, SmartCity360°: virtual event, December 2-4, 2021: proceedings". Berlín: Springer, 2021, p. 155-171. ISBN 978-3-031-06371-8. DOI 10.1007/978-3-031-06371-8_11.
ISBN978-3-031-06371-8
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-031-06371-8_11
Col·leccions
- Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [292]
- Computer Sciences - Ponències/Comunicacions de congressos [574]
- CAP - Grup de Computació d'Altes Prestacions - Ponències/Comunicacions de congressos [784]
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.954]
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Large-Scale-VA-through-OLC-post-print.pdf | 575,4Kb | Visualitza/Obre |