Boosting discriminative models for activity detection using local feature descriptors. Pham, V. H., Le, M. H., & Van-Dung, H. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10191 LNAI, pages 609–618, 2017. ISSN: 16113349doi abstract bibtex This paper presents a method for daily living activity prediction based on boosting discriminative models. The system consists of several steps. First, local feature descriptors are extracted from multiple scales of the sequent images. In this experiment, the basic feature descriptors based on HOG, HOF, MBH are considered to process. Second, local features based BoW descriptors are studied to construct feature vectors, which are then fed to classification machine. The BoW feature extraction is a pre-processing step, which is utilized to avoid strong correlation data, and to distinguish feature properties for uniform data for classification machine. Third, a discriminative model is constructed using the BoW features, which is based on the individual local descriptor. Sequentially, final decision of action classes is done by the classifier using boosting discriminative models. Different to previous contributions, the sequent-overlap frames are considered to convolute and infer action classes instead of an individual set of frames is used for prediction. An advantage of boosting is that it supports to construct a strong classifier based on a set of weak classifiers associated with appropriate weights to obtain results in high performance. The method is successfully tested on some standard databases.
@inproceedings{Pham2017,
title = {Boosting discriminative models for activity detection using local feature descriptors},
volume = {10191 LNAI},
isbn = {978-3-319-54471-7},
doi = {10.1007/978-3-319-54472-4_57},
abstract = {This paper presents a method for daily living activity prediction based on boosting discriminative models. The system consists of several steps. First, local feature descriptors are extracted from multiple scales of the sequent images. In this experiment, the basic feature descriptors based on HOG, HOF, MBH are considered to process. Second, local features based BoW descriptors are studied to construct feature vectors, which are then fed to classification machine. The BoW feature extraction is a pre-processing step, which is utilized to avoid strong correlation data, and to distinguish feature properties for uniform data for classification machine. Third, a discriminative model is constructed using the BoW features, which is based on the individual local descriptor. Sequentially, final decision of action classes is done by the classifier using boosting discriminative models. Different to previous contributions, the sequent-overlap frames are considered to convolute and infer action classes instead of an individual set of frames is used for prediction. An advantage of boosting is that it supports to construct a strong classifier based on a set of weak classifiers associated with appropriate weights to obtain results in high performance. The method is successfully tested on some standard databases.},
booktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},
author = {Pham, Van Huy and Le, My Ha and Van-Dung, Hoang},
year = {2017},
note = {ISSN: 16113349},
keywords = {Action recognition, Boosting discriminative models, Histograms of oriented gradients, Motion boundary, Optical flow},
pages = {609--618},
}
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The BoW feature extraction is a pre-processing step, which is utilized to avoid strong correlation data, and to distinguish feature properties for uniform data for classification machine. Third, a discriminative model is constructed using the BoW features, which is based on the individual local descriptor. Sequentially, final decision of action classes is done by the classifier using boosting discriminative models. Different to previous contributions, the sequent-overlap frames are considered to convolute and infer action classes instead of an individual set of frames is used for prediction. An advantage of boosting is that it supports to construct a strong classifier based on a set of weak classifiers associated with appropriate weights to obtain results in high performance. 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