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Real-Time Object Detection Cascades

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.

Applications:
Face Detection

    

Car Detection

    

Pedestrian Detection     

Logo Detection
Target Logo Demo Pepsi Logo Demo

    


Publications: High Detection-rate Cascades for Real-Time Object Detection.
Hamed Masnadi-Shirazi and Nuno Vasconcelos
Proceedings of IEEE International Conference on Computer Vision (ICCV) ,
Rio de Janeiro, Brazil, 2007.
IEEE, [pdf]
  Asymmetric Boosting
Hamed Masnadi-Shirazi and Nuno Vasconcelos
Proceedings of International Conference on Machine Learning (ICML),
Corvallis, OR, May 2007.
[pdf]
Contact: Nuno Vasconcelos, Hamed Masnadi-Shirazi

 



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