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Introduction

Introduction. How to improve results : don’t use pixel, use feature combination! Consequence : high dimensional data (>170,000 dimensions!) Solution : Partial Least Squares (PLS). Introduction. Works: Face recognition (ECCV 2010) Pedestrian detection (ICCV 2009)

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Introduction

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Presentation Transcript

  1. Introduction • How to improve results: don’t use pixel, use feature combination! • Consequence: high dimensional data (>170,000 dimensions!) • Solution: Partial Least Squares (PLS)

  2. Introduction • Works: • Face recognition (ECCV 2010) • Pedestrian detection (ICCV 2009) • Human Detection under Partial Occlusion (ICB 2009) • Appearance-based modeling (SIBGRAPI 2009) • Data-driven detection optimization (under submission) Pedestriandetection (ICCV 2009)

  3. Introduction • Characteristics of humans in standing positions: • Strong vertical edges along the boundaries of the body; • Clothing is generally uniform. Clothing textures are different from natural textures; • Discriminatory color information is found in the face/head regions;

  4. Introduction • Therefore, edges, colors and textures capture important cues for discriminating humans from the background. • Features used: • Histogram of Oriented Gradient (HOG) descriptors; • Color frequency; • Texture features computed from co-occurrence matrices;

  5. Introduction • Consequences of feature augmentation: • High dimensional feature spaces (> 170,000); • The number of samples for training is much smaller than features; • Sampling with overlapping regions increases the multicollinearity of the feature set; • An ideal setting for a statistical technique known as Partial Least Squares (PLS).

  6. Partial Least Squares • PLS is a class of methods for modeling relations between sets of variables using latent spaces. • Regression and class aware dimensionality reduction. • Feature vectors are projected onto projection vectors estimated by PLS, then a classifier is used in the resulting low dimensional sub-space.

  7. Partial Least Squares PLS: PCA: • X: data matrix (n x p) • y: response variable (n x 1) • W: projection matrix (p x m) • PLS constructs orthogonal latent components

  8. Framework Input Image Detection windows Detection window split into overlapping blocks (blocks of multiple sizes) Feature extraction Dimensionality reduction Final Result Probability map PLS model Classification

  9. Feature Combination • Features extract different information. • Combination leads to an improvement over single feature-based approach.

  10. Experimental Results INRIA Pedestrian Dataset DaimlerChrysler Dataset (170,820 features) (33,312 features)

  11. Experimental Results

  12. Conclusions • Combination of different features leads to more accurate results. • This richer set of features analyzed by PLS provides improvements in human detection.

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