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Conference Papers Year : 2018

SIMILARITY LEARNING WITH LISTWISE RANKING FOR PERSON RE-IDENTIFICATION

Andrei Stoian
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  • PersonId : 964723
Jean-Yves Dufour
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Abstract

Person re-identification is an important task in video surveillance systems. It consists in matching an image of a probe person among a gallery image set of people detected from a network of surveillance cameras with non-overlapping fields of view. The main challenge of person re-identification is to find image representations that are discriminating the per-sons' identities and that are robust to the viewpoint, body pose, illumination changes and partial occlusions. In this paper , we proposed a metric learning approach based on a deep neural network using a novel loss function which we call the Rank-Triplet loss. This proposed loss function is based on the predicted and ground truth ranking of a list of instances instead of pairs or triplets and takes into account the improvement of evaluation measures during training. Through our experiments on two person re-identification datasets, we show that the new loss outperforms other common loss functions and that our approach achieves state-of-the-art results on these two datasets.
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Dates and versions

hal-01895355 , version 1 (15-10-2018)

Identifiers

  • HAL Id : hal-01895355 , version 1

Cite

Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. SIMILARITY LEARNING WITH LISTWISE RANKING FOR PERSON RE-IDENTIFICATION. International conference on image processing, Oct 2018, Athenes, Greece. ⟨hal-01895355⟩
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