A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models
This addresses the challenge of person re-identification for forensic, security, and surveillance systems, but it is incremental as it builds on existing feature descriptor and metric learning approaches.
The paper tackled the problem of person re-identification by proposing a new feature descriptor model using a multi-level Gaussian framework on pixel features, achieving superior performance with the highest accuracy on four public datasets compared to state-of-the-art methods.
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.