IEEE Trans Image Process - Efficiently learning a detection cascade with sparse eigenvectors.

Tópicos

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Resumo

Real-time object detection has many computer vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce greedy sparse linear discriminant analysis (GSLDA) for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with . Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample reweighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions (e.g., face detection) demonstrate that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that AdaBoost and similar approaches are not the only methods that can achieve high detection results for real-time object detection.

Resumo Limpo

realtim object detect mani comput vision applic sinc viola jone propos first realtim adaboost base face detect system much effort spent improv boost method work first show featur select method boost can also use train effici object detector particular introduc greedi spars linear discrimin analysi gslda conceptu simplic comput effici slight better detect perform achiev compar moreov propos new techniqu term boost greedi spars linear discrimin analysi bgslda effici train detect cascad bgslda exploit sampl reweight properti boost classsepar criterion gslda experi domain high skew data distribut eg face detect demonstr classifi train propos bgslda outperform adaboost variant find provid signific opportun argu adaboost similar approach method can achiev high detect result realtim object detect

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