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 language | English |
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| Title of host publication | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | IEEE |
| Pages | 1515-1519 |
| Number of pages | 5 |
| Volume | 2021-June |
| ISBN (Electronic) | 978-1-7281-7605-5 |
| DOIs | |
| Publication status | Published - 2021 |
| Publication type | A4 Article in conference proceedings |
| Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 https://2021.ieeeicassp.org |
Publication series
| Name | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
|---|---|
| ISSN (Print) | 1520-6149 |
| ISSN (Electronic) | 2379-190X |
Conference
| Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 6/06/21 → 11/06/21 |
| 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