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Identity Inference: Generalizing Person Re-identification Scenarios

  • Svebor Karaman
  • Andrew D. Bagdanov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

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.

Keywords

Test Image Spatial Pyramid Label Problem Gallery Image Unlabeled Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Svebor Karaman
    • 1
  • Andrew D. Bagdanov
    • 1
  1. 1.Media Integration and Communication CenterUniversity of FlorenceFlorenceItaly

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