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