Object detection techniques : overview and performance comparison

Noman, Mohammed and Stankovic, Vladimir and Tawfik, Ayman (2019) Object detection techniques : overview and performance comparison. In: 2019 IEEE International Symposium on Signal Processing and Information Technology, 2019-12-10 - 2019-12-12, Ajman University Conference Center. (In Press)

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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.

    Creators(s): Noman, Mohammed, Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Tawfik, Ayman;
    Item type:Conference or Workshop Item(Paper)
    ID code:70438
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:object detection, Tensorflow, Azure Cloud, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:06 Nov 2019 12:39
    Last modified:04 Dec 2020 02:56
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/70438
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