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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. - PowerPoint PPT Presentation
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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.
Papageorgiou, and Poggio. Trainable Pedestrian Detection. International Conference on Image Processing 1999.
Motivation
Recognition system inside vehicles
Valerie – detect and greet those who stop in front of the booth
Overview
Positive samples Negative samples
Classifier
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”
Wavelet Template
-1 1
vertical horizontal diagonal
-1
1
-11
Average of many samples
Features
Each image is one instance with 1326 features and one classification
Same thing for negative samples
Test case 282 positive samples, 236 negative
samples for training 20 positives and 20 negatives for testing
Some Positive Samples
Some negative samples
Results
Nearest neighbor classifier 95% accuracy
Decision tree classifier 90% accuracy
2 false positives 3 false positives, 1 false negative
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
Incremental bootstrapping Use nearest neighbor
But problem with many false positives
Incremental bootstrapping Took database of 558 total samples After bootstrapping, 656 total samples
Bootstrapping
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
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
Splitted up into 560 images, about 30 classified as positive
Some false positives
true positives
Results
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
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)
Limitations Recognize only template,
other objects may be similar
Difficult to define what is a negative sample
What if pedestrians are partially occluded?