2013 | OriginalPaper | Buchkapitel
Multi-target Data Association Using Sparse Reconstruction
verfasst von : Andrew D. Bagdanov, Alberto Del Bimbo, Dario Di Fina, Svebor Karaman, Giuseppe Lisanti, Iacopo Masi
Erschienen in: Image Analysis and Processing – ICIAP 2013
Verlag: Springer Berlin Heidelberg
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In this paper we describe a solution to multi-target data association problem based on ℓ
1
-regularized sparse basis expansions. Assuming we have sufficient training samples per subject, our idea is to create a discriminative basis of observations that we can use to reconstruct and associate a new target. The use of ℓ
1
-regularized basis expansions allows our approach to exploit multiple instances of the target when performing data association rather than relying on an average representation of target appearance. Preliminary experimental results on the PETS dataset are encouraging and demonstrate that our approach is an accurate and efficient approach to multi-target data association.