Problem: SVM training is expensive– Mining for hard

negatives, bootstrapping

Solution: LDA (Linear Discriminant Analysis). – Extremely fast

training, very similar performance

Claim

Linear Discriminant Analysis (LDA) Assumptions

Learning - Classification

ImplementationFeatures

a simple procedure that allows us to learn a and a (corresponding to the background) once, and then reuse it for every window size N and for every object category.

Implementation

Mean

Covariance

Regularization

• Very large

• In my experiments 10, for making sure that is PSD.

Covariance

Fast training using LDA

Use in clustering

Clustering in WHO Space

Clustering in WHO Space

HOG WHO

Clustering in WHO Space

HOG WHO

(a) SVM

Pedestrian DetectionLinear Discriminant Models

SVM

LDA

Cen

Pedestrian DetectionLinear Discriminant Models

Results

Results

Method Mean AP Train complexity

Test complexity

ESVM + Co-occ 22.6 High High

ESVM + Calibr 19.8 High High

ELDA + Calibr 19.1 Low High

Ours full 21.0 Low Low

Results

Pascal NN Classification

Summary

• Whitened for HOG is better than HOG

• LDA for fast training of hog templates– Object Independent Background (?)

• mean better represents the cluster compared to the medoid– Use all the samples rather than 1

• Their statistical models also suggest that natural image statistics, largely ignored in the field of object detection, are worth (re)visiting.