Human action recognition using transfer learning with deep representations

Bux, Allah and Wang, Xiaofeng and Angelov, Plamen Parvanov and Habib, Zulfiqar (2017) Human action recognition using transfer learning with deep representations. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE. ISBN 9781509061839

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Abstract

Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human action recognition based on a pre-trained deep CNN model for feature extraction & representation followed by a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier for action recognition. It has been observed that already learnt CNN based representations on large-scale annotated dataset could be transferred to action recognition task with limited training dataset. The proposed method is evaluated on two well-known action datasets, i.e., UCF sports and KTH. The comparative analysis confirms that the proposed method achieves superior performance over state-of-the-art methods in terms of accuracy.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 87182
Deposited By: ep_importer_pure
Deposited On: 29 Jan 2018 09:08
Refereed?: Yes
Published?: Published
Last Modified: 24 Feb 2020 04:39
URI: https://eprints.lancs.ac.uk/id/eprint/87182

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