Density-Based Vehicle Counting with Unsupervised Scale Selection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138878" target="_blank" >RIV/00216305:26230/20:PU138878 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.dicta2020.org/wp-content/uploads/2020/09/22_CameraReady.pdf" target="_blank" >http://www.dicta2020.org/wp-content/uploads/2020/09/22_CameraReady.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DICTA51227.2020.9363401" target="_blank" >10.1109/DICTA51227.2020.9363401</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Density-Based Vehicle Counting with Unsupervised Scale Selection

Popis výsledku v původním jazyce

A significant hurdle within any counting task is the variance in scale of the objects to be counted. While size changes of some extent can be induced by perspective distortion, more severe scale differences can easily occur, e.g. in case of images taken by a  drone from different elevations above the ground. The aim of our work is to overcome this issue by leveraging only lightweight dot annotations and a minimum level of training supervision. We propose a modification to the Stacked Hourglass network which enables the model to process multiple input scales and to automatically select the most suitable candidate using a quality score. We alter the training procedure to enable learning of the quality scores while avoiding their direct supervision, and thus without requiring any additional annotation effort. We evaluate our method on three standard datasets: PUCPR+, TRANCOS and CARPK. The obtained results are on par with current state-of-the-art methods while being more robust towards significant variations in input scale.

Název v anglickém jazyce

Density-Based Vehicle Counting with Unsupervised Scale Selection

Popis výsledku anglicky

A significant hurdle within any counting task is the variance in scale of the objects to be counted. While size changes of some extent can be induced by perspective distortion, more severe scale differences can easily occur, e.g. in case of images taken by a  drone from different elevations above the ground. The aim of our work is to overcome this issue by leveraging only lightweight dot annotations and a minimum level of training supervision. We propose a modification to the Stacked Hourglass network which enables the model to process multiple input scales and to automatically select the most suitable candidate using a quality score. We alter the training procedure to enable learning of the quality scores while avoiding their direct supervision, and thus without requiring any additional annotation effort. We evaluate our method on three standard datasets: PUCPR+, TRANCOS and CARPK. The obtained results are on par with current state-of-the-art methods while being more robust towards significant variations in input scale.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Digital Image Computing: Techniques and Applications 2020

ISBN

978-1-7281-9108-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Melbourne

Místo konání akce

Melbourne

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000935148000034