2. Problem: SVM training is expensive – Mining for hard negatives, bootstrapping Solution: LDA (Linear Discriminant Analysis). – Extremely fast training, very similar performance Claim

3. Linear Discriminant Analysis (LDA) Assumptions Learning - Classification

4. 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.

5. Implementation Mean Covariance

6. Regularization Very large In my experiments 10, for making sure that is PSD.

7. Covariance

8. Fast training using LDA

9. Use in clustering

10. Clustering in WHO Space

11. HOG WHO

12. Clustering in WHO Space HOG WHO

13. (a) SVM Pedestrian Detection Linear Discriminant Models

14. SVM LDA Cen Pedestrian Detection Linear Discriminant Models

15. Results

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

17. Results

18. Pascal NN Classification

19. 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.