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Loris Bazzani Marco Cristani

"Person re-identification: a recent issue for the videosurveillance community and a technique for approaching it. ?. Loris Bazzani Marco Cristani. Modena, 17 maggio 2011. Before we start…. Download code and datasets for the exercises ( iLIDS , VIPeR , CAVIAR) :

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Loris Bazzani Marco Cristani

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  1. "Person re-identification: a recent issue for the videosurveillance community and a technique for approaching it ? Loris Bazzani Marco Cristani Modena, 17 maggio 2011

  2. Before we start… • Download code and datasets for the exercises (iLIDS, VIPeR, CAVIAR): http://profs.scienze.univr.it/~bazzani/TMP/S4_SDALF_reid.zip • [opt.] Check out our CVPR 2010 paper: http://www.lorisbazzani.info/papers/proceedings/FarenzenaetalCVPR10.pdf • [opt.] Check out the website: http://www.lorisbazzani.info/code-datasets/sdalf-descriptor/

  3. Outline of the lesson • Person Re-identification (few minutes…) • A possible solution: SDALF, Symmetry-Driven Accumulation of Local Features (20 minutes…) • Matlab exercises (~1 hour)

  4. Person Re-identification • Goal: Recognizing an individual in different timings Same camera T = 145 T = 222 Different overlapping cameras T = 1 T = 23 Different non overlapping cameras

  5. Person Re-identification • Issues: • Many, you will see them in the exercises…

  6. A possible solution: SDALF, Symmetry-Driven Accumulation of Local Features • Overview of the proposed descriptor: For each body part STEP 3: Per-region Feature STEP 0-1: Axes of Symmetry and Asymmetry Descriptor Accumulation t STEP 2: Chromatic Feature STEP 4: Texture Feature

  7. Step 0 – Isolating the silhouette • We need to focus on the body of the person • We perform background subtraction or • We apply a statistical model of the human appearance [Jojic et al. 2009]

  8. Step 1 – Axes of (A)simmetry • We draw axes of symmetry and asymmetry • Features near the axes of symmetry are more reliable

  9. Step 1 – Axes of (A)simmetry Chromatic operator Spatial covering operator BG subtraction using STEL generative model

  10. Step 2 - Chromatic feature • For each part (no head), we compute a weighted color histograms • HSV color space • “Gaussian Kernel” for each body part: • Low-weight to the background clutter • Robust to illumination changes, partial occlusions

  11. Step 3 - Per-region feature • Maximally Stable Color Region (MSCR) detector • Detect “stable blobs” • Look at successive steps of an agglomerative clustering of image pixels • Covariant to affine transformations Clustering of the detected blobs to reduce the computational cost of the matching

  12. Step 4 - Texture feature • Recurrent High-Structured Patches (RHSP) detector

  13. Accumulation of features • Descriptor: • Single-shot: SDALF with only one image (no accumulation) • Multi-shot: SDALF with multiple images

  14. Testing the person re-identification methods A (probe) B (gallery) Rank Pick a selection

  15. Matching algorithm • Distance between two signatures WHERE Bhattacharyya distance between HSV histograms , Distances between blob descriptors

  16. How to evaluate • Cumulative Matching Characteristic (CMC) curve,the expectation of finding the correct match in the top n matches

  17. Ex. 1: The Datasets • Exercise 1: take a look at the datasets and try to find out the challenges of the re-id problem For this, you can use the MATLAB file: DEMO0_dataset.m

  18. Ex. 2: SDALF • Exercise 2: qualitative analysis of the SDALF descriptor: display the weighted HSV hist., MSCR, RHSP For this, you can use the MATLAB file: DEMO1_SDALFextraction.m

  19. Ex. 3: Cross-validation • Exercise 3: try the cross-validation code evaluating CMC, SRR and nAUC • Compare SvsS and MvsM case • Vary the number of images for the MvsM case For this, you can use the MATLAB file: DEMO2_crossvalid.m [set MAXCLUSTER=1 (SvsS) or >1 (MvsM)]

  20. Ex 4: Matching • Exercise 4: evaluate qualitatively the output of the matching procedure For this, you can use the MATLAB file: DEMO2_crossvalid.m [set plotMatch=1] And DEMO3_crossvalid.m

  21. Take-home Message • Why this lesson? • To be able to use our system on new datasets • Compare your personal methods with SDALF Questions?

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