Bidirectional sparse representations for multi-shot person re-identification

Abstract : With the development of surveillance cameras, person re-identification has gained much interest, however re-identifying people across cameras remains a challenging problem which not only requires a good feature description but also a reliable matching scheme. Our method can be applied with any feature and focuses on the second requirement. We propose a robust bidirectional sparse coding method that improves simple sparse coding performances. Some recent work have already explored sparse representation for the re-identification task but none has considered the problem from both the probe and the gallery perspectives. We propose a bidirectional sparse representations method which searches for the most likely match for the test element in the gallery set and makes sure that the selected gallery match is indeed closely related to the probe. Extensive experiments on two datasets, CUHK03 and iLIDS-VID, show the effectiveness of our approach.