Sparse Re-Id: Block Sparsity for Person Re-Identification

This paper presents a novel approach to solve the problem of person re-identification in non-overlapping camera views. We hypothesize that the feature vector of a probe image approximately lies in the linear span of the corresponding gallery feature vectors in a learned embedding space. We then formulate the re-identification problem as a block sparse recovery problem and solve the associated optimization problem using the alternating directions framework. We evaluate our approach on the publicly available PRID 2011 and iLIDS-VID multi-shot re-identification datasets and demonstrate superior performance in comparison with the current state of the art.