Unsupervised Construction of Task-Specific Datasets for Object Re-identification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00350725" target="_blank" >RIV/68407700:21240/21:00350725 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/21:00548651

Result on the web

<a href="https://doi.org/10.1145/3477911.3477922" target="_blank" >https://doi.org/10.1145/3477911.3477922</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3477911.3477922" target="_blank" >10.1145/3477911.3477922</a>

Result language

angličtina

Original language name

Unsupervised Construction of Task-Specific Datasets for Object Re-identification

Original language description

In the last decade, we have seen a significant uprise of deep neural networks in image processing tasks and many other research areas. However, while various neural architectures have successfully solved numerous tasks, they constantly demand more and more processing time and training data. Moreover, the current trend of using existing pre-trained architectures just as backbones and attaching new processing branches on top not only increases this demand but diminishes the explainability of the whole model. Our research focuses on combinations of explainable building blocks for the image processing tasks, such as object tracking. We propose a combination of Mask R-CNN, state-of-the-art object detection and segmentation neural network, with our previously published method of sparse feature tracking. Such a combination allows us to track objects by connecting detected masks using the proposed sparse feature tracklets. However, this method cannot recover from complete object occlusions and has to be assisted by an object re-identification. To this end, this paper uses our feature tracking method for a slightly different task: an unsupervised extraction of object representations that we can directly use to fine-tune an object re-identification algorithm. As we have to use objects masks already in the object tracking, our approach utilises the additional information as an alpha channel of the object representations, which further increases the precision of the re-identification. An additional benefit is that our fine-tuning method can be employed even in a fully online scenario.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ICCTA 2021 Conference Proceedings

ISBN

978-1-4503-9052-1

ISSN

—

e-ISSN

—

Number of pages

7

Pages from-to

66-72

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Vídeň

Event date

Jul 13, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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