Pregled bibliografske jedinice broj: 485488
Streamlining collection of training samples for object detection and classification in video
Streamlining collection of training samples for object detection and classification in video // Proceedings of the 33rd international convention of information and communication technology, electronics and microelectronics (MIPRO 2010) / Bogunović, Nikola ; Ribarić, Slobodan (ur.).
Zagreb: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2010. str. 215-220 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 485488 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Streamlining collection of training samples for object detection and classification in video
Autori
Bulović, Ana ; Bučar, Damir ; Palašek, Petar ; Popović, Bojan ; Trbojević, Ante ; Zadrija, Lucija ; Kusalić, Ivan ; Brkić, Karla ; Kalafatić, Zoran ; Šegvić, Siniša
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 33rd international convention of information and communication technology, electronics and microelectronics (MIPRO 2010)
/ Bogunović, Nikola ; Ribarić, Slobodan - Zagreb : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2010, 215-220
ISBN
978-953-233-053-3
Skup
The 33rd international convention of information and communication technology, electronics and microelectronics (MIPRO 2010)
Mjesto i datum
Opatija, Hrvatska, 24.05.2010. - 28.05.2010
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
computer vision
Sažetak
This paper is concerned with object recognition and detection in computer vision. Many promising approaches in the field exploit the knowledge contained in a collection of manually annotated training samples. In the resulting paradigm, the recognition algorithm is automatically constructed by some machine learning technique. It has been shown that the quantity and quality of positive and negative training samples is critical for good performance of such approaches. However, collecting the samples requires tedious manual effort which is expensive in time and prone to error. In this paper we present design and implementation of a software system which addresses these problems. The system supports an iterative approach whereby the current state-of- the-art detection and recognition algorithms are used to streamline the collection of additional training samples. The presented experiments have been performed in the frame of a research project aiming at automatic detection and recognition of traffic signs in video.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
036-0361935-1954 - Teorija, modeliranje i uporaba autonomno orijentiranih računarskih struktura (Ribarić, Slobodan, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb