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Pedestrian Recognition

Pedestrian Recognition. Machine Perception and Modeling of Human Behavior Manfred Lau. Pedestrian Recognition. Oren, Papageorgiou, Sinha, Osuna, Poggio. Pedestrian Detection Using Wavelet Templates. CVPR 1997.

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Pedestrian Recognition

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  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 samples Negative samples Classifier

  5. Wavelet Template -1 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. -1 1 -1 -1 1 1 vertical horizontal diagonal Wavelet Template 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 positives 3 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?

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