High Detection-Rate Cascades for Real-Time Object Detection

Abstract

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, at the cost of a marginal increase in computation.