Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU117039" target="_blank" >RIV/00216305:26230/15:PU117039 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/DICTA.2015.7371318" target="_blank" >http://dx.doi.org/10.1109/DICTA.2015.7371318</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DICTA.2015.7371318" target="_blank" >10.1109/DICTA.2015.7371318</a>

Result language

angličtina

Original language name

Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance

Original language description

This paper deals with unsupervised collection of information from traffic surveillance video streams. Deployment of usable traffic surveillance systems requires minimizing of efforts per installed camera - our goal is to enroll a new view on the street without any human operator input. We propose a method of automatically collecting vehicle samples from surveillance cameras, analyze their appearance and fully automatically collect a fine-grained dataset. This dataset can be used in multiple ways, we are explicitly showcasing the following ones: fine-grained recognition of vehicles and camera calibration including the scale. The experiments show that based on the automatically collected data, make&model vehicle recognition in the wild can be done accurately: average precision 0.890. The camera scale calibration (directly enabling automatic speed and size measurement) is twice as precise as the previous existing method. Our work leads to automatic collection of traffic statistics without the costly need for manual calibration or make&model annotation of vehicle samples. Unlike most previous approaches, our method is not limited to a small range of viewpoints (such as eye-level cameras shots), which is crucial for surveillance applications.

Czech name

—

Czech description

—

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2015

Confidentiality

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

Article name in the collection

Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on

ISBN

978-1-4673-6795-0

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

Australian Pattern Recognition Society

Place of publication

Adelaide

Event location

Adelaide

Event date

Nov 23, 2015

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000380485600107