Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Learning multi-instance sub-pixel point localization

Schroeter, Julien, Sidorov, Kirill ORCID: https://orcid.org/0000-0001-7935-4132 and Marshall, Andrew ORCID: https://orcid.org/0000-0003-2789-1395 2021. Learning multi-instance sub-pixel point localization. Presented at: 15th Asian Conference on Computer Vision (ACCV 2020), Virtual (Kyoto), 30 November - 4 December 2020. Published in: Ishikawa, H., Liu, CL., Pajdla, T. and Shi, J. eds. Proceedings of Computer Vision – ACCV 2020. Springer, pp. 669-686. 10.1007/978-3-030-69541-5_40
Item availability restricted.

[thumbnail of ACCV 2020 | Learning Multi-Instance Sub-pixel Point Localization.pdf] PDF - Accepted Post-Print Version
Restricted to Repository staff only

Download (1MB)

Abstract

In this work, we propose a novel approach that allows for the end-to-end learning of multi-instance point detection with inherent sub-pixel precision capabilities. To infer unambiguous localization estimates, our model relies on three components: the continuous prediction capabilities of offset-regression-based models, the finer-grained spatial learning ability of a novel continuous heatmap matching loss function introduced to that effect, and the prediction sparsity ability of count-based regularization. We demonstrate strong sub-pixel localization accuracy on single molecule localization microscopy and checkerboard corner detection, and improved sub-frame event detection performance in sport videos.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Professional Services > Advanced Research Computing @ Cardiff (ARCCA)
Schools > Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-030-69540-8
Date of First Compliant Deposit: 14 December 2020
Date of Acceptance: 16 September 2020
Last Modified: 31 Jul 2025 11:13
URI: https://orca.cardiff.ac.uk/id/eprint/136988

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics