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.
@article{arxiv.2102.04798,
title = {Ensembling object detectors for image and video data analysis},
author = {Kateryna Chumachenko and Jenni Raitoharju and Alexandros Iosifidis and Moncef Gabbouj},
journal= {arXiv preprint arXiv:2102.04798},
year = {2021}
}
Comments
Accepted to ICASSP 2021.(C)2021 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