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

TL;DR

This paper introduces a multi-level Gaussian model-based feature descriptor for person re-identification, capturing local and global image information to improve accuracy in challenging scenarios.

Contribution

It proposes a novel multi-level Gaussian framework that encodes local image regions and compares it with existing metric learning methods for enhanced re-identification.

Findings

Achieved superior accuracy on four challenging datasets.

Demonstrated the effectiveness of multi-level Gaussian modeling.

Compared favorably against state-of-the-art methods.

Abstract

Person re-identification is being widely used in the forensic, and security and surveillance system, but person re-identification is a challenging task in real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of Gaussian distribution model on pixel features, which include color moments, color space values 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 earlier metric learning methods on this descriptor. The performance of the descriptor is evaluated on four public available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-arts, which demonstrate the superior performance.