Multidimensional Sparse Representation for Multishot Person Reidentification

Person reidentification is known as recognizing a subject in diverse scenes obtained from nonoverlapping cameras. To the best of our knowledge, the existing sparse representation approaches for person reidentification need an additional stage for combining a different level of features. In this letter, we propose a new sparse representation approach based on tensor along with the dictionary learning that is able to tackle both the sparse representation of features and the combination of different level of features, simultaneously. First, we construct the feature tensors using images of people. Subsequently, we learn a single cross-view invariant dictionary for representing images from different viewpoints in each tensor mode. The tensor representations of images alleviate the computational complexity of the conventional feature combination approaches, and enhance the reidentification of high dimensional data. Experimental results on iLIDS-VID dataset show the superiority of our method compared to some recent methods.