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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 samplesNegative samples Classifier
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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”
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Wavelet Template 1 vertical horizontal diagonal 1 1 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 positives3 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?
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