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

Implementation Features 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

HOG WHO

Clustering in WHO Space HOG WHO

(a) SVM Pedestrian Detection Linear Discriminant Models

SVM LDA Cen Pedestrian Detection Linear Discriminant Models

Results

MethodMean APTrain complexity Test complexity ESVM + Co-occ22.6High ESVM + Calibr19.8High ELDA + Calibr19.1LowHigh Ours full21.0Low

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.