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People, penguins and Petri dishes: adapting object counting models to new visual domains and object types without forgetting

Marsden, Mark, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Keogh, Ciara E. and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2018) People, penguins and Petri dishes: adapting object counting models to new visual domains and object types without forgetting. In: Computer Vision and Pattern Recognition 2018, 18-22 Jun 2018, Salt Lake City, USA.

Abstract
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model to adjust to the statistical distributions of the various visual domains. The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife. As part of this study a challenging new cell counting dataset in the context of tissue culture and patient diagnosis is constructed. This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is the first of its kind to be made available to the wider computer vision community. State-of-the-art object counting performance is achieved in both the Shanghaitech (parts A and B) and Penguins datasets while competitive performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets, all using a shared counting model.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Crowd Analysis; Domain Adaptation
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Proceedings. . IEEE.
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/CVPR.2018.00842
Copyright Information:© 2018 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council and Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2289 and 15/SIRG/3283.
ID Code:22264
Deposited On:25 Jun 2018 10:04 by Mark Andrew Marsden . Last Modified 05 Jan 2022 16:59
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