Work

Ensembling Object Detectors for Image and Video Data Analysis

Year: 2021

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp39728.2021.9414013

Authors: Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj

Institutions: Tampere University, University of Tampere, Finnish Environment Institute, Aarhus University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Business Finland

Award: G4248650291