Improved Person Re-Identification Using Statistical Approximation
Person re-identification on image sets in which each image is taken from a different angle and lighting condition is a very challenging task. This task becomes even more difficult when images are low resolution and carrying image compression artifacts. The accuracy of the existing re- identification techniques are relatively low on the challenging evaluation grounds such as VIPeR and iLIDS image datasets. In these datasets, distortions in shape and colour make the re- identification task difficult and uncertain for both machine and human. In this paper, we propose a new approach to address the uncertainty in low resolution images for person re-identification by using statistical approximation. We first show that the distribution within a patch on person's image does not fit a normal distribution via Kolmogorov- Smirnov test. Then we simplify the Kolmogorov- Smirnov statistic by using only the mean and standard deviation of the distribution. These values are used as descriptors for per region per channel, and concatenated for comparison of image pairs. Experiments show that the proposed approach outperforms the state-of-the-art on person re- identification methods. The small memory foot print and the low computational cost of the proposed technique make it suitable for person re- identification in large scale surveillance applications.