Ensembling object detectors for image and video data analysis
Chumachenko, Kateryna; Raitoharju, Jenni; Iosifidis, Alexandros; Gabbouj, Moncef (2021)
Chumachenko, Kateryna
Raitoharju, Jenni
Iosifidis, Alexandros
Gabbouj, Moncef
IEEE
2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202302102246
https://urn.fi/URN:NBN:fi:tuni-202302102246
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [16944]