1. Pedestrian Recognition Machine Perception and Modeling of Human Behavior Manfred Lau

2. Pedestrian Recognition  Oren, Papageorgiou, Sinha, Osuna, Poggio. Pedestrian Detection Using Wavelet Templates. CVPR 1997.  Papageorgiou, and Poggio. Trainable Pedestrian Detection. International Conference on Image Processing 1999.

3. Motivation  Recognition system inside vehicles  Valerie – detect and greet those who stop in front of the booth

4. Overview Positive samplesNegative samples Classifier

5. Wavelet Template 1 vertical wavelet Average of many samples  Compute coefficient for each RGB channel and take largest absolute value  Vertical wavelet identifies “vertical color differences”

6. Wavelet Template 1 vertical horizontal diagonal 1 1 Average of many samples

7. Features  Each image is one instance with 1326 features and one classification  Same thing for negative samples

8. Test case  282 positive samples, 236 negative samples for training  20 positives and 20 negatives for testing Some Positive Samples

9. Some negative samples

10. Results Nearest neighbor classifier 95% accuracy Decision tree classifier 90% accuracy 2 false positives3 false positives, 1 false negative

11. 10-fold cross validation  Test case: 302 positives, 256 negatives  Nearest neighbor  94.27% 30 false positives, 2 false negatives  Decision tree  86.74% 47 false positives, 27 false negatives

12. Incremental bootstrapping  Use nearest neighbor But problem with many false positives

13. Incremental bootstrapping  Took database of 558 total samples  After bootstrapping, 656 total samples

14. Bootstrapping

15. Result  A completely new test image  Before bootstrapping 85.06% accurate, 65 false pos, 0 false neg  After bootstrapping 90.11% accurate, 43 false pos, 0 false neg

16. Result  Another new test image  Before bootstrapping 75.86% accurate, 100 false pos, 5 false neg  After bootstrapping 81.15% accurate, 77 false pos, 5 false neg

17. Splitted up into 560 images, about 30 classified as positive Some false positives true positives

18. Results

19. Less features  Take average coefficients across many positive samples  Pick those features that are darkest/lightest  can use much less than 1326 features, for faster classification

20. Conclusions  Can detect positive samples well, but many false positives  Bootstrapping on more and more new images will decrease false positives (I’m not doing enough of this)

21. Limitations  Recognize only template, other objects may be similar  Difficult to define what is a negative sample  What if pedestrians are partially occluded?