A Deep Structure of Person Re-Identification Using Multi-Level Gaussian Models

Abstract

Person re-identification is being widely used in the forensic, and security and surveillance system these days. However, it is still a challenging task in a real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of the Gaussian distribution model on pixel features, which include color moments, color space values, gradient information, and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes these local Gaussians for each region, creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of some existing metric learning methods on this descriptor. The performance of the descriptor is evaluated on four publicly available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-art works. It clearly demonstrates the superior performance of the proposed descriptor.