In this article we introduce the problem of identity inference as a generalization of the re-identification problem. Identity inference is applicable in situations where a large number of unknown persons must be identified without knowing a priori that groups of test images represent the same individual. Standard single- and multi-shot person re-identification are special cases of our formulation. We present an approach to solving identity inference problems using a Conditional Random Field (CRF) to model identity inference as a labeling problem in the CRF. The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space, and is flexible enough to incorporate constraints in the temporal and spatial domains. Experimental results are given on the ETHZ dataset. Our approach yields state-of-the-art performance for the multi-shot re-identification task and promising results for more general identity inference problems.

Identity inference: Generalizing person re-identification scenarios / Karaman, Svebor; Bagdanov, Andrew D.. - ELETTRONICO. - 7583:(2012), pp. 443-452. (Intervento presentato al convegno 12th European Conference on Computer Vision, ECCV 2012 tenutosi a Florence, ita nel 2012) [10.1007/978-3-642-33863-2_44].

Identity inference: Generalizing person re-identification scenarios

KARAMAN, SVEBOR;BAGDANOV, ANDREW DAVID
2012

Abstract

In this article we introduce the problem of identity inference as a generalization of the re-identification problem. Identity inference is applicable in situations where a large number of unknown persons must be identified without knowing a priori that groups of test images represent the same individual. Standard single- and multi-shot person re-identification are special cases of our formulation. We present an approach to solving identity inference problems using a Conditional Random Field (CRF) to model identity inference as a labeling problem in the CRF. The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space, and is flexible enough to incorporate constraints in the temporal and spatial domains. Experimental results are given on the ETHZ dataset. Our approach yields state-of-the-art performance for the multi-shot re-identification task and promising results for more general identity inference problems.
2012
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12th European Conference on Computer Vision, ECCV 2012
Florence, ita
2012
Karaman, Svebor; Bagdanov, Andrew D.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1081459
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