Abstract
In this paper we address the problem of re-identification of people: given a camera network with non-overlapping fields of view, we study the problem of how to correctly pair detections in different cameras (one to many problem, search for similar cases) or match detections to a database of individuals (one to one, search for best match case). We propose a novel color histogram based features which increases the re-identification rate. Furthermore we evaluate five different classifiers: three fixed distance metrics, one learned distance metric and a classifier based on sparse representation, novel to the field of re-identification. A new database alongside with the matlab code produced are made available on request.
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Figueira, D., Bernardino, A. (2011). Re-identification of Visual Targets in Camera Networks: A Comparison of Techniques. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_30
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DOI: https://doi.org/10.1007/978-3-642-21593-3_30
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