Machine learning for object detection

Zuo Xiang, Renbing Zhang, Patrick Seeling

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 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 languageEnglish
Title of host publicationComputing in Communication Networks
Subtitle of host publicationFrom Theory to Practice
PublisherElsevier
Pages325-338
Number of pages14
ISBN (Electronic)9780128204887
DOIs
StatePublished - Jan 1 2020

Keywords

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

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