Program

Introduction:

Time: 40 minutes, slides  9 am – 9.40 am

Objective: In this first part of the tutorial we introduce the basic scope of person re-identification, the nature of the problem, issues, datasets, modalities and why this approach became so popular lately.

The introduction to the topic is broke down in the following items:

  • Definition of the person re-identification problem;
  • Why person re-identification is useful [4];
  • Use cases:
    • Re-acquire people in camera networks;
    • Recognize people in biometric systems;
    • Automatic profiling of customers/visitors/tourists;
  • Introduction of the basic scenarios, modalities [2] and probe/gallery paradigm.
  • Introduction of the main datasets (VIPeR, CAVIAR4REID, iLIDS, ETHZ, 3DPeS, CMV100, etc.) used for algorithm evaluation [4]
  • Presentation of the standard evaluation tools (Cumulative Match Characteristic, CMC) [4] and clarifications about general vocabulary (recognition rate at first rank, etc.).

Person Representation

Time: 40 minutes, slides  9.40 am – 10.20 am

Objective: In this part we present techniques that try to build a discriminative representation of a person while preserving viewpoint and illumination invariance.

This topic is organized as follows:

  • How to describe an image containing a person.
  • Which cues are important?
    • Colors
    • Texture
    • Saliency
  • Is background/foreground segmentation important?
  • Is part segmentation important?
  • Overview of main representation milestones:
    • SDALF descriptor [1]
    • WHOS descriptor [2]
    • Other descriptors
    • Short, practical MATLAB session on how to compute common descriptors given an image

Matching People across Views

Time: 40 minutes, slides   10.20 am – 11.00 am

Objective: In this part of the tutorial we address how to match different people considering the probe/gallery paradigm and how to better exploit the gallery images that describe a target. We then introduce different matching strategies from a baseline (Nearest Neighbor) matcher to more complex statistical models that try to solve the gap of matching people across non-overlapping cameras.

This topic is organized as follows:

  • Recap on Modalities and probe/gallery paradigm
  • Nearest Neighbor (NN)
  • NN with combined distances (SDALF) [1]
  • ISR (Sparse Iterative Ranking) method [2]
  • Metric Learning methods
  • KCCA-based (Kernel Canonical Correlation Analysis) method [2, 3]
  • Short, practical MATLAB session of matching methods.

Coffe Break

 Time: 10 min , 11.00 am – 11.10 am

Practical Code Session

 Time: 25 min,  code session, 11.10 am – 11.35 am

Objective: In this part of the tutorial we show and run some didactical MATLAB tool to perform some re-identification tasks:

  1. Person Description
  2. Matching
  3. Method Evaluation

A case of study, Mnemosyne project

Time: 25 min, slides  11.35 am – 12.00 pm

Objective: In this final part, we discuss a real application of person re-identification. The system is used to profile tourist interests while they are walking in a museum in order to improve their experience about the artwork. We discuss solved problems along with open issues and we introduce a new dataset acquired in this scenario.

This topic closes the tutorial as follows:

  • Introduction of the Mnemosyne project: scope, issues and solved problems [5]
  • Show some results and a video demo.
  • Discuss open issues in real-world applications.
  • Wrap-up and conclusions

 

References:

  1. M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani, “Person re-identification by symmetry-driven accumulation of local features,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit.,
    2010, pp. 2360–2367.
  2. Lisanti, G.; Masi, I.; Bagdanov, A.D.; Bimbo, A.D., “Person Re-Identification by Iterative Re-Weighted Sparse Ranking,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.8, pp.1629,1642, Aug. 1 2015
    doi: 10.1109/TPAMI.2014.2369055
  3. Giuseppe Lisanti , Iacopo Masi , Alberto Del Bimbo, Matching People across Camera Views using Kernel Canonical Correlation Analysis”, Eighth ACM/IEEE International Conference on Distributed Smart Cameras, 2014.
  4. D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models
    for recognition, reacquisition, and tracking,” in Proc. 10th IEEE
    Workshop Perform. Eval. Tracking Surveillance, 2007, pp. 41–48.
  5. Federico Bartoli, Giuseppe Lisanti, Lorenzo Seidenari, Svebor Karaman, Alberto Del Bimbo,”MuseumVisitors: a dataset for pedestrian and group detection, gaze estimation and behavior understanding”, Proc. of CVPR Int’l. Workshop on Int.’l Workshop on Group And Crowd Behavior Analysis And Understanding – 2015

Comments are closed.