1. EECS 274 Computer Vision Object detection

2. Human detection HOG features Cue integration Ensemble of classifiers ROC curve Reading: Assigned papers

3. Human detection with HOG Histogram of oriented gradients Using local gradients to represent positive and negative examples

4. Histogram of oriented gradients

5. HOG descriptors

6. Results with MIT dataset

7. Results with INRIA dataset

8. Parameter sweeping

9. Block/cell size

10. Results

11. Observations No gradient smoothing with [-1,0,1] derivative filter Use gradient magnitude (no thresholding) Orientation voting into fine bins Spatial voting into coarser bins Strong local normalization Overlapping normalization blocks

12. Cal Tech Pedestrian Dataset A large annoated dataset with performance evaluation

13. Performance evaluation

14. Results (cont’d)

18. Summary HOG, MultiFtr, FtrMine outperform others VJ and Shaplet perform poorly LatSvm trained on PASCAL dataset HOG poerforms best on near, unoccluded pedestrians MultiFtr ties or outperforms HOG on difficult cases Much room for imporvment

19. Daimler dataset Recent survey in PAMI 09 Observation –HOG/linSVM at higher image resolution performs well, with lower processing speed) –Wavelet-based Adaboost cascade at lower image resolution performs well, with higher processing speed

20. Neural network with receptive fields

21. Results

22. Cue integration Multi-cue pedestrian detection and tracking from a moving vehicle, IJCV 06

23. Classifier ensemble Cascade of boosted classifiers Variable-size blocks: 12 x 12, 64 x 128, etc.  5031 blocks in 64 x 128 image patch Fast human detection using a cascade of histograms of oriented gradients, CVPR 06

24. Classifier ensemble An HOG-LBP Human Detector with Partial Occlusion Handling, ICCV 09

25. Convert holistic classifier to local-classifier ensemble An HOG-LBP Human Detector with Partial Occlusion Handling, ICCV 09 ?