Loris Bazzani*, Marco Cristani*

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Multiple-shot Person Re-identification by HPE
signature

• Loris Bazzani, Marco Cristani, Alessandro
  Perina, Michela Farenzena, Vittorio Murino
• Computer Science Department, University of
  Verona, Italy
• Istituto Italiano di Tecnologia (IIT), Genova,
  Italy

This research is founded by the EU-Project FP7
SAMURAI,grant FP7-SEC- 2007-01 No. 217899
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Analysis of the problem (1)

• Person Re-identification Recognizing an
  individual in diverse locations over different
  (non-)overlapping camera views

Different cameras
T 1
T 23
Same camera
T 145
T 222
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Analysis of the problem (2)

• We focus on the problem with non-overlapping
  cameras
• Problems in real scenarios
• Very low resolution
• Severe Occlusions
• Illumination variations
• Pedestrians with very similar clothes
• Pose and view-point changes
• No geometry of the environment
• Solution
• Histogram Plus Epitome (HPE) descriptor, and
• Multiple-shot approach

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Outline

• Overview of the proposed method
• Pre-processing Background Subtraction
• Images selection for Multiple-shot
• HPE descriptor
• Global descriptor
• Local descriptors
• HPEs Matching
• Results
• Conclusions

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Overview of the proposed method

• Employing global and local appearance-based
  features
• Exploiting the temporal consistency to make
  robust the descriptor

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Background Subtraction

• We employ a novel generative model STEL Jojic
  el al. 2009
• Capture the structure of an image class as a
  mixture of component segmentations
• Isolate meaningful parts that exhibit tight
  feature distributions

Learned Mixture Components
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Images selection for Multiple-shot

• Objective discard redundant information and
  images with occlusions
• Gaussian Mixture Models Clustering Figueiredo
  and Jain 2002 of HSV histograms
• Automatic model selection employing the Bayesian
  Information Criterion Figueiredo and Jain 2002
• Discard the clusters with low number of instances
• Keep a random instance for each cluster
• Examples of ruled-out examples

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HPE descriptor Global feature

• Capture chromatic global information

• 36-dimensional HSV histogram (H16, S16, V4)
• Average the histograms of the multiple instances
• Robust to illumination and pose variations,
  keeping the predominant chromatic information only

Caused by illumination changes
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HPE descriptor Local feature (1)

• Epitome Jojic el al. 2003 generative model
  that analyzes the presence of recurrent,
  structured local patterns

Generic Epitome
Local Epitome
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HPE descriptor Local feature (2)

• Generic Epitome
• 36-dimensional HSV histogram of the Epitome
• Local Epitome
• Keep the patches with high
  probability that a patch in the epitome having
  (i, j) as left-upper corner represents several
  ingredient patches
• Discard the patches with low entropy
• Extract a 36-dimensional HSV histogram of the
  survived patches

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HPEs Matching

• Re-identification associating each element in
  the probe set B to the corresponding element in
  the gallery set A
• Minimize the following distance
• where is the Bhattacharyya distance and

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Results (1)

• iLIDS dataset
• Multiple images of 119 pedestrians 128x64 pixels
• Comparison with Context-based method Zheng et
  al. 2009
• Cross-validation SvsS 10 trials, MvsS/MvsM 100
  trials

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Results (2)

• ETHZ dataset
• Three datasets of 83, 35 and 28 pedestrians of
  64x32 pixels
• Comparison with Partial Least Square (PLS) method
  Schwartz and Davis 2009
• Cross-validation Settings as for iLIDS

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Results (3)

• How many images do we need to perform a good
  person re-identification?

N 5 seems to be the best trade-off
N Number of images for the multi-shot approach
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Conclusions

• We proposed a novel descriptor for the person
  re-identification problem, i.e., HPE descriptor
• The descriptor is robust to low resolution,
  occlusions, illumination variations, pedestrians
  with very similar clothes, pose changes
• It is based on the accumulation of images to gain
  robustness
• Person re-identification problem is still far
  from being solved
• The results suggest that further improvements can
  be reached

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References

• Jojic el al. 2009 N. Jojic, A. Perina, M.
  Cristani, V. Murino, and B. Frey, Stel component
  analysis Modeling spatial correlations in image
  class structure, IEEE Conference on Computer
  Vision and Pattern Recognition, pp. 20442051,
  2009.
• Figueiredo and Jain 2002 M. Figueiredo and A.
  Jain, Unsupervised learning of finite mixture
  models, IEEE Trans. PAMI, vol. 24, no. 3, pp.
  381396, 2002.
• Jojic el al. 2003 N. Jojic, B. J. Frey, and A.
  Kannan, Epitomic analysis of appearance and
  shape, in IEEE International Conference on
  Computer Vision. Washington, DC, USA IEEE
  Computer Society, 2003, p. 34.
• Schwartz and Davis 2009 W. Schwartz and L.
  Davis, Learning discriminative appearance-based
  models using partial least squares, in
  XXIISIBGRAPI, 2009.
• Zheng et al. 2009 W. Zheng, S. Gong, and T.
  Xiang, Associating groups of people, in BMVC,
  2009.