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| Real-Time Object Detection Cascades | |||||||||||||||||||||
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A new strategy is proposed for the design of cascaded
object detectors of high detection-rate. The problem of
jointly minimizing the false-positive rate and classification
complexity of a cascade, given a constraint on its detection
rate, is considered. It is shown that it reduces to the
problem of minimizing false-positive rate given detection rate
and is, therefore, an instance of the classic problem of
cost-sensitive learning. A cost-sensitive extension of boosting,
denoted by asymmetric boosting, is introduced. It
maintains a high detection-rate across the boosting iterations,
and allows the design of cascaded detectors of high
overall detection-rate. Experimental evaluation shows that,
when compared to previous cascade design algorithms, the
cascades produced by asymmetric boosting achieve significantly
higher detection-rates.
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