We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.


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    Titel :

    People Recognition in Image Sequences by Supervised Learning


    Beteiligte:

    Erscheinungsdatum :

    2000


    Format / Umfang :

    4611797 byte , 373760 byte


    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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