Al-Obaidi, S., Al-Khafaji, H. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2021) Making sense of neuromorphic event data for human action recognition. IEEE Access, 9. pp. 82686-82700. ISSN 2169-3536
Abstract
Neuromorphic vision sensors provide low power sensing and capture salient spatial-temporal events. The majority of the existing neuromorphic sensing work focus on object detection. However, since they only record the events, they provide an efficient signal domain for privacy aware surveillance tasks. This paper explores how the neuromorphic vision sensor data streams can be analysed for human action recognition, which is a challenging application. The proposed method is based on handcrafted features. It consists of a pre-processing step for removing the noisy events followed by the extraction of handcrafted local and global feature vectors corresponding to the underlying human action. The local features are extracted considering a set of high-order descriptive statistics from the spatio-temporal events in a time window slice, while the global features are extracted by considering the frequencies of occurrences of the temporal event sequences. Then, low complexity classifiers, such as, support vector machines (SVM) and K-Nearest Neighbours (KNNs), are trained using these feature vectors. The proposed method evaluation uses three groups of datasets: Emulator-based, re-recording-based and native NVS-based. The proposed method has outperformed the existing methods in terms of human action recognition accuracy rates by 0.54%, 19.3%, and 25.61% for E-KTH, E-UCF11 and E-HMDB51 datasets, respectively. This paper also reports results for three further datasets: E-UCF50, R-UCF50, and N-Actions, which are reported for the first time for human action recognition on neuromorphic vision sensor domain.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Neuromorphic vision sensing (NVS); event cameras; dynamic vision sensing (DVS); human action recognition (HAR); local features; global features |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jun 2021 11:09 |
Last Modified: | 25 Jun 2021 11:09 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/access.2021.3085708 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175216 |