Human Re-Identification In Surveillance Network

This paper presents an appearance-based model to address the human re-identification problem. Human reidentification is an important and still unsolved task in computer vision. Re-identification refers to the problem of establishing correspondence among various observations of the same subject viewed at different time instances in different camera positions. It can also be defined as the process to match persons observed in non overlapping camera views with visual features for inter-camera tracking. The ambiguity increases with the number of candidates to be distinguished. Simple temporal reasoning can simplify the problem by pruning the candidate set to be matched. In many systems there is a requirement to identify individuals or determine whether a given individual has already appeared over a network of cameras. The human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people a human signature should handle difference in illumination pose and camera parameters. The paper focuses on a new appearance model based on Mean Riemannian Covariance (MRC) patches extracted from tracks of a particular individual to capturing from a distance even at low resolution without active co-operation of subjects, has motivated us to use it for re-identification in the computer surveillance network.