Machine learning for object detection

Zuo Xiang; Renbing Zhang; Patrick Seeling

doi:10.1016/B978-0-12-820488-7.00034-7

Machine learning for object detection

Zuo Xiang, Renbing Zhang, Patrick Seeling

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Scopus citations

Abstract

Artificial Intelligence (AI) and its applications have continuously gained traction in recent years, with visual understanding as part of Computer Vision (CV) representing one particularly demanding application. Especially for real-time scenarios, the stringent QoS requirements combined with high bandwidth needs for image or video data represent a particular challenge. In this chapter, we describe the implementation and evaluation of a distributed object recognition service within Service Function Chainings (SFCs), which can be optimal for deploying object detection services, especially when facing simultaneous network traffic and low latency requirement challenges. In the hands-on examples of this chapter an optimized distributed AI approach to real-time visual understanding will be evaluated within the ComNetsEmu environment.

Original language	English
Title of host publication	Computing in Communication Networks
Subtitle of host publication	From Theory to Practice
Publisher	Elsevier
Pages	325-338
Number of pages	14
ISBN (Electronic)	9780128204887
DOIs	https://doi.org/10.1016/B978-0-12-820488-7.00034-7
State	Published - Jan 1 2020

Keywords

Computing-in-network
Convolutional neutral network
Deep learning
Object detection
Service function chaining

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10.1016/B978-0-12-820488-7.00034-7

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Machine learning for object detection

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Keywords

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