Distributed Edge Cloud R-CNN for Real Time Object Detection
| Autor: | Parsa Yousefi, Mevlut A. Demir, John J. Prevost, Joshua Herrera, Paul Rad |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Real-time computing Cloud computing 02 engineering and technology 010501 environmental sciences 01 natural sciences Pipeline (software) Convolutional neural network Object detection Data-driven Proof of concept 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution business Edge computing 0105 earth and related environmental sciences |
| Zdroj: | WAC |
| DOI: | 10.23919/wac.2018.8430385 |
| Popis: | Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high-volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks, these performance improvements only focus on a single, contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN pipeline. By splitting the object detection pipeline into components and dynamically distributing these components in the cloud, we can achieve optimal performance to enable real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform including cloud servers and edge-embedded devices for real-time object detection on live video streams. |
| Databáze: | OpenAIRE |
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