Dense appearance modeling and efficient learning of camera transitions for person re-identification

Martin Hirzer, Csaba Beleznai, Martin Köstinger, Peter Roth, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning. In this work, we aim at taking advantage of both directions of research. On the one hand side, we compute a descriptive appearance representation encoding the vertical color structure of pedestrians. To improve the classification results, we additionally estimate the transition between two cameras using a pair-wisely estimated metric. In particular, we introduce 4D spatial color histograms and adopt Large Margin Nearest Neighbor (LMNN) metric learning. The approach is demonstrated for two publicly available datasets, showing competitive results, however, on lower computational costs.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Image Processing (ICIP)
Publication statusPublished - 2012
EventInternational Conference on Image Processing - Orlando, United States
Duration: 30 Sept 20123 Oct 2012


ConferenceInternational Conference on Image Processing
Country/TerritoryUnited States

Fields of Expertise

  • Information, Communication & Computing


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