In this paper we describe a solution to multi-target data association problem based on l1-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 l1-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.
BAGDANOV, A.D., DEL BIMBO, A., DI FINA, D., KARAMAN, S., LISANTI, G., MASI, I. (2013). Multi-target Data Association Using Sparse Reconstruction. Springer Berlin Heidelberg [10.1007/978-3-642-41184-7_25].
Multi-target Data Association Using Sparse Reconstruction
LISANTI, GIUSEPPE;
2013
Abstract
In this paper we describe a solution to multi-target data association problem based on l1-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 l1-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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
