Ensembling object detectors for image and video data analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We further extend it to video data by proposing a two-stage tracking-based scheme for detection refinement. The proposed method can be used as a standalone approach for improving object detection performance, or as a part of a framework for faster bounding box annotation in unseen datasets, assuming that the objects of interest are those present in some common public datasets.

Original languageEnglish
Title of host publication ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1515-1519
Number of pages5
Volume2021-June
ISBN (Electronic)978-1-7281-7605-5
DOIs
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021
https://2021.ieeeicassp.org

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Country/TerritoryCanada
CityToronto
Period6/06/2111/06/21
Internet address

Keywords

  • Bounding box annotation
  • Ensemble models
  • Object detection

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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