Pregled bibliografske jedinice broj: 827059
Deep Metric Learning for Person Re-Identification and De-Identification
Deep Metric Learning for Person Re-Identification and De-Identification // Proceedings of the 39th International Convention MIPRO 2016 / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016. str. 1454-1458 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 827059 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Deep Metric Learning for Person Re-Identification and De-Identification
Autori
Filković, Ivan ; Kalafatić, Zoran ; Hrkać, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 39th International Convention MIPRO 2016
/ Biljanović, Petar - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016, 1454-1458
ISBN
978-953-233-087-8
Skup
MIPRO 2016 Special Session on Biometrics, Forensics, De-Identification and Privacy Protection /BiForD
Mjesto i datum
Opatija, Hrvatska, 30.05.2016. - 03.06.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
person de-identification ; person re-identification ; deep learning
Sažetak
Large amounts of visual data are gathered from various surveillance systems across different places and times, and have to be processed in order to infer the current state of the world. One of the common problems in surveillance scenarios is person re-identification, the task of associating a person across different cameras. On the other hand, these scenarios raise privacy concerns, which lead to the need for person deidentification, i.e. concealing person identity. This task is related to the re-identification in two aspects: (i) multiple appearances of the same person could be de- identified in similar manner ; and (ii) if we discover the features useful for re- identification, we could try to hide the identity by modifying those features. Re- identification can be addressed as a classification problem. The state-of-the art classification methods are based on deep learning. In this paper we explore the applicability of the recently proposed Triplet network architecture to the person re- identification problem, by applying it on VIPeR dataset. We show that the network is able to learn useful feature-space embeddings, and analyze its benefits and limitations.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
