Rapid-Rich Object Search Lab

Person Re-Identification

Rahul Rama Varior, Asst. Prof. Wang Gang

Person re-identification deals with matching images of same person over multiple non-overlapping camera views. It is applicablein tracking a particular person across these cameras, tracking the trajectory of a person, surveillance, and for forensic and security applications.

  • Multi-camera tracking: To track an individual using multiple cameras, the identity of the person has to be retrieved from the second camera based on the information obtained from the first camera.
  • Tracking the trajectory: If the locations of the cameras are known, based on the re-identification system, it is possible to track the moving path of a person from one point to another. Eg: From Indoor office spaces, to shopping malls, to MRT stations, etc.
  • Surveillance and security applications: To track the suspect of a crime scene, using multiple cameras. Intended for forensic applications.

Some example images from VIPeR[1] dataset which is publicly available for research purposes:


Some of the major challenges for person re-identification system that is being addressed in the research community are

  1. Illumination changes: Day light intensity, shade, changes in illumination color in indoor and outdoor environments etc. This causes changes in the appeared color of the same subject across multiple camera views.
  2. Resolution: As many of the older surveillance cameras give low-resolution images, it is a difficult task for the algorithm to differentiate between individuals due to lack of “information”.
  3. Occlusion: In crowded scenes, it is difficult to obtain full-body image of an individual due to partial or full occlusion of that individual by others in the crowd.
  4. Uniform Clothing: For schools and some of the factory/construction sites, clothing is uniform for the subjects. If the algorithm is mainly based on the appearance, it is difficult to distinguish between subjects.
  5. Scalability: Public areas are equipped with thousands of cameras. Current technologies are only starting to address multi-camera surveillance problems.
  6. Lack of annotated data: Obtaining data is easy. But annotated data is always scarce. For good generalization capabilities, complex algorithms need to be taught with large number of labelled data.

Existing Technologies
Most of the re-identification research works are carried out focusing on two aspects of the problem. To develop a mathematical representation for the images, known as a feature representation, and to develop a distance function that can reduce the “distance” between individuals of same identity and increase the “distance” between individuals of different identity in an n-dimensional vector space.

Our proposed systems address two key problems of person re-identification.
  1. Illumination: An illumination invariant descriptor based on new technologies such as feature learning is proposed to handle appearance changes due to varying illumination.
  2. Labelled data: To tackle the problem of lack of annotated data, distance learning using marginalization is proposed.

A short demo of our results is given below.

[1] D. Gray, S. Brennan, and H. Tao. Evaluating appearance models for recognition, reacquisition, and tracking. In PETS, 2007. URL: https://vision.soe.ucsc.edu/node/178