Person Reidentification With Reference Descriptor

Abstract

Person identification across non overlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.

Existing System

In existing Pattern matching using a reference set has been explored in different fields. It generated fixed-length codes for indexing biometric databases. The index codes were constructed by computing match scores between a biometric image and a fixed set of images. The dissimilarity space to convert the structural representation of data to a dissimilarity representation using a representation set and some suggestions for prototype selection were provided.

Proposed System

A reference-based method is proposed for person reidentification across different cameras. The matching is conducted in a reference space where the descriptor for a person is translated from the original color. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA).For reidentification, the gallery data and the probe data are projected onto this RCCA subspace.The reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data.The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery.

Architecture