In this paper we consider the problem of classifying people spatial orientation with respect to the camera viewpoint from 2D images. Structured multi-class feature selection allows us to control the amount of redundancy of our input data, while semi-supervised learning helps us coping with the intrinsic ambiguity of output labels. We model the multi-class classification problem with an all-pairs strategy based on the use of a coding matrix. A thorough experimental evaluation on the TUD Multiview Pedestrian benchmark dataset demonstrates the superiority of our approach w.r.t. state-of-the-art.
Semi-supervised learning of sparse representations to recognize people spatial orientation
NOCETI, NICOLETTA;ODONE, FRANCESCA
2014-01-01
Abstract
In this paper we consider the problem of classifying people spatial orientation with respect to the camera viewpoint from 2D images. Structured multi-class feature selection allows us to control the amount of redundancy of our input data, while semi-supervised learning helps us coping with the intrinsic ambiguity of output labels. We model the multi-class classification problem with an all-pairs strategy based on the use of a coding matrix. A thorough experimental evaluation on the TUD Multiview Pedestrian benchmark dataset demonstrates the superiority of our approach w.r.t. state-of-the-art.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
