Abstract
This work investigates whether virtual samples can solve the small sample size (S 3 ) problem of Keep-It-Simple-and-Straightforward Metric Learning (KISSME) in Pedestrian re-identification (Re-ID). Re-ID is a very challenging and important problem in the field of multi-camera surveillance in smart transportation. Among many hand-crafted ways (not deeply-learned ones) to solve it, KISSME has received great attention. Although it has achieved convincing performance in some applications, it encounters an S 3 problem in calculating various classes of covariance matrices whose eigenvalues become too small. Such small eigenvalues cause an instability issue when computing the inverse of covariance matrices, thus resulting in poor Re-ID performance. If we can increase the number of samples, then an S 3 problem is alleviated or eliminated. This work makes a hypothesis that virtual samples can do so, and proposes a new algorithm to generate them. It adopts a Genetic Algorithm to generate virtual features (corresponding to virtual samples) based on the dimension-reduced sample features, which eliminates the process of re-extracting features of newly generated virtual samples and save time. It can clearly increase the magnitude of otherwise small eigenvalues, helps one perform the accurate estimation of the inverse of various covariance matrices and finally alleviates the S 3 problem. Experimental results based on a commonly-used database confirm that the proposed method can significantly improve the matching rate of pedestrian Re-ID, which fully shows that virtual samples are indeed effective for alleviating the S 3 problem in pedestrian Re-ID.