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Jiahe Li
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Detection examples
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Overview
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Network architecture
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Notation Input Output
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Sample collection
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Positive sample
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Negative sample
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Loss function
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Datasets Caltech CityPersons 11 sets of videos: Train (6), Test (5)
Reasonable ([0, 0.35]), Partial ([0.01, 0.35]), Heavy ([0.36, 0.8]) CityPersons Train (2975), Val (500) and Test (1575) Reasonalbe, Small ([50,75], [0,0.35]), Heavy ([50), [0.35,0.8]), All ([20), [0, 0.8])
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Evaluation metric Log-average miss rate
Averaging miss rates at 9 false positives per image (FPPI) point sevenly spaced between 10−2 and 1 in log space
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Fast R-CNN
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Faster R-CNN
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Experimental results
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Experimental results
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Experimental results
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Experimental results
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Conclusion A bi-box regression approach A training strategy
Full body estimation Visible part estimation A training strategy A new criterion Positive sample
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