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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2019

24.09.2018 | Original Article

Human activity recognition using mixture of heterogeneous features and sequential minimal optimization

verfasst von: Humza Naveed, Gulraiz Khan, Asad Ullah Khan, Aiman Siddiqi, Muhammad Usman Ghani Khan

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2019

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Abstract

Automated detection and tracking of a person’s actions plays a vital role in surveillance systems. Human activity detection has been carried out by using a variety of features; including flow-based, spatio-temporal and interest points based. We have created a fusion of features by incorporating those which give better results. LBP, HOG, Haar wavelets, SIFT, velocity and displacement being the major ones. By employing the time efficiency and optimality of SMO to train SVM, we have trained our system for both single person and multi-human action classification with improved accuracy. A generalized hierarchy of actions has been presented in this paper to demonstrate the extension of our methodology. We have achieved an accuracy of 91.99% on combination of KTH and Weizmann dataset and 86.48% on multi-human dataset. We have introduced our self-generated multi-human activity dataset in the following paper.
Vorheriger Artikel Filter-based unsupervised feature selection using Hilbert–Schmidt independence criterion
Nächster Artikel A projection-based approach to software quality evaluation from the users’ perspectives

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Metadaten
Titel
Human activity recognition using mixture of heterogeneous features and sequential minimal optimization
verfasst von
Humza Naveed
Gulraiz Khan
Asad Ullah Khan
Aiman Siddiqi
Muhammad Usman Ghani Khan
Publikationsdatum
24.09.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0870-1

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