Person Re-identification for Real-world Surveillance Systems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2016

Person Re-identification for Real-world Surveillance Systems


Appearance based person re-identification in a real-world video surveillance system with non-overlapping camera views is a challenging problem for many reasons. Current state-of-the-art methods often address the problem by relying on supervised learning of similarity metrics or ranking functions to implicitly model appearance transformation between cameras for each camera pair, or group, in the system. This requires considerable human effort to annotate data (see Section 1.1). Furthermore, the learned models are camera specific and not transferable from one set of cameras to another. Therefore, the annotation process is required after every network expansion or camera replacement, which strongly limits their applicability. Alternatively, we propose a novel mod-eling approach to harness complementary appearance information without supervised learning that significantly outperforms current state-of-the-art unsupervised methods on multiple benchmark datasets.

Dates and versions

hal-01399939 , version 1 (21-11-2016)



Furqan M Khan, François M Brémond. Person Re-identification for Real-world Surveillance Systems. 2016. ⟨hal-01399939⟩
192 View
0 Download



Gmail Facebook X LinkedIn More