Action recognition with cascaded feature selection and classification
Action recognition with cascaded feature selection and classification
- Author(s): M. Bregonzio ; Shaogang Gong ; Tao Xiang
- DOI: 10.1049/ic.2009.0252
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- Author(s): M. Bregonzio ; Shaogang Gong ; Tao Xiang Source: 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), 2009 page ()
- Conference: 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)
- DOI: 10.1049/ic.2009.0252
- ISBN: 978 1 84919 207 1
- Location: London, UK
- Conference date: 3 Dec. 2009
- Format: PDF
Much of the previous action recognition work focuses on action representation whilst using standard multi-class classifiers such as SVM and k-NN for action classification. We show that these standard classifiers are inadequate in addressing more challenging action recognition problems encountered in an unconstrained environment and propose a novel action classification approach based on cascaded feature selection and classification. Instead of separating multiple action classes simultaneously, the more difficult single task is decomposed automatically into easier sub-tasks of separating two groups of the most separable action classes at a time with different features selected for different sub-tasks. Experiments are carried out using challenging public datasets to demonstrate that with identical action representation, our cascaded classifier significantly outperforms standard multi-class classifiers. (6 pages)
Inspec keywords: video surveillance; object recognition; feature extraction; image classification
Subjects: Video signal processing; Computer vision and image processing techniques; Image recognition
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