Object detection techniques: overview and performance comparison

Mohammed Noman, Vladimir Stankovic, Ayman Tawfik

Research output: Contribution to conferencePaper

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Abstract

Object detection algorithms are improving by the minute. There are many common libraries or application program interface (APIs) to use. The most two common techniques ones are Microsoft Azure Cloud object detection and Google Tensorflow object detection. The first is an online-network based API, while the second is an offline-machine based API. Both have their advantages and disadvantages. A direct comparison between the most common object detection methods help in finding the best solution for advance system integration. This paper will discuss both methods and compare them in terms of accuracy, complexity and practicality. It will show advantages and also limitations of each method, and possibilities for improvement.
Original languageEnglish
Number of pages4
Publication statusAccepted/In press - 4 Nov 2019
Event2019 IEEE International Symposium on Signal Processing and Information Technology - Ajman University Conference Center, Ajman, United Arab Emirates
Duration: 10 Dec 201912 Dec 2019
https://www.isspit.org/isspit/2019/

Conference

Conference2019 IEEE International Symposium on Signal Processing and Information Technology
Abbreviated titleISSPIT
CountryUnited Arab Emirates
CityAjman
Period10/12/1912/12/19
Internet address

Keywords

  • object detection
  • Tensorflow
  • Azure Cloud

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