Pedestrian Detection from Low Resolution Public Cameras in the Wild

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127222" target="_blank" >RIV/00216305:26220/18:PU127222 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SPIN.2018.8474255" target="_blank" >http://dx.doi.org/10.1109/SPIN.2018.8474255</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SPIN.2018.8474255" target="_blank" >10.1109/SPIN.2018.8474255</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pedestrian Detection from Low Resolution Public Cameras in the Wild

Popis výsledku v původním jazyce

Since security situation in the world is changing, monitoring of protected areas using surveillance systems has been of increased significance in the recent years. Although the today object detection methods significantly improved accuracy, for real situations, where the video is stream basically of a low resolution and objects are often small and blurry, the methods are still struggling with precise detection. The key parts of any security system are 1) person detection and then also 2) person recognition, which must perform in real-time processing. This paper deals with pedestrian detection in so called wild - i.e. from sources with bad quality, blurry images or small objects for detection. We used Single Shot MultiBox Detector (SSD) which was trained on VOC 2007 dataset and using fine-tuning it achieved percentage increase 11.98% of accuracy for life scenarios. Thus, SSD confirmed its state-of-the-art position and ability to be simply adapted to specific cases of detection while keeping its high performance.

Název v anglickém jazyce

Pedestrian Detection from Low Resolution Public Cameras in the Wild

Popis výsledku anglicky

Since security situation in the world is changing, monitoring of protected areas using surveillance systems has been of increased significance in the recent years. Although the today object detection methods significantly improved accuracy, for real situations, where the video is stream basically of a low resolution and objects are often small and blurry, the methods are still struggling with precise detection. The key parts of any security system are 1) person detection and then also 2) person recognition, which must perform in real-time processing. This paper deals with pedestrian detection in so called wild - i.e. from sources with bad quality, blurry images or small objects for detection. We used Single Shot MultiBox Detector (SSD) which was trained on VOC 2007 dataset and using fine-tuning it achieved percentage increase 11.98% of accuracy for life scenarios. Thus, SSD confirmed its state-of-the-art position and ability to be simply adapted to specific cases of detection while keeping its high performance.

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)

ISBN

978-1-5386-3045-7

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

291-295

Název nakladatele

Neuveden

Místo vydání

New Delhi, India

Místo konání akce

Dept. of ECE, ASET, Amity University, Noida Sec-

Datum konání akce

22. 2. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000446953700055