Matching people across views is still an open problem in computer vision and in video surveillance systems. In this paper we address the problem of person re-identification across disjoint cameras by proposing an efficient but robust kernel descriptor to encode the appearance of a person. The matching is then improved by applying a learning technique based on Kernel Canonical Correlation Analysis (KCCA) which finds a common subspace between the proposed de- scriptors extracted from disjoint cameras, projecting them into a new description space. This common description space is then used to identify a person from one camera to another with a standard nearest-neighbor voting method. We evaluate our approach on two publicly available datasets for re-identification (VIPeR and PRID), demonstrating that our method yields state-of-the-art performance with respect to recent techniques proposed for the re-identification task.

Matching People Across Camera Views Using Kernel Canonical Correlation Analysis / G. Lisanti;I. Masi;A. Del Bimbo. - ELETTRONICO. - (2014), pp. 79-84. (Intervento presentato al convegno International Conference on Distributed Smart Cameras tenutosi a Venezia (Italia) nel 2014) [10.1145/2659021.2659036].

Matching People Across Camera Views Using Kernel Canonical Correlation Analysis

LISANTI, GIUSEPPE;MASI, IACOPO;DEL BIMBO, ALBERTO
2014

Abstract

Matching people across views is still an open problem in computer vision and in video surveillance systems. In this paper we address the problem of person re-identification across disjoint cameras by proposing an efficient but robust kernel descriptor to encode the appearance of a person. The matching is then improved by applying a learning technique based on Kernel Canonical Correlation Analysis (KCCA) which finds a common subspace between the proposed de- scriptors extracted from disjoint cameras, projecting them into a new description space. This common description space is then used to identify a person from one camera to another with a standard nearest-neighbor voting method. We evaluate our approach on two publicly available datasets for re-identification (VIPeR and PRID), demonstrating that our method yields state-of-the-art performance with respect to recent techniques proposed for the re-identification task.
2014
Proceedings of the International Conference on Distributed Smart Cameras
International Conference on Distributed Smart Cameras
Venezia (Italia)
2014
G. Lisanti;I. Masi;A. Del Bimbo
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/947562
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