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MultiWave-Net: An Optimized Spatiotemporal Network for Abnormal Action Recognition Using Wavelet-Based Channel Augmentation

Elmasry, Ramez M. ; Abd El Ghany, Mohamed A. ; Salem, Mohammed A.-M. ; Fahmy, Omar M. (2024)
MultiWave-Net: An Optimized Spatiotemporal Network for Abnormal Action Recognition Using Wavelet-Based Channel Augmentation.
In: AI, 2024, 5 (1)
doi: 10.26083/tuprints-00027246
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (Abstract)

Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in this work, abnormal behavior is regarded as human aggression or in another context when car accidents occur on the road. As this behavior can negatively affect the surrounding traffic participants, such as vehicles and other pedestrians, it is crucial to monitor such behavior. Given the current prevalent spread of cameras everywhere with different types, they can be used to classify and monitor such behavior. Accordingly, this work proposes a new optimized model based on a novel integrated wavelet-based channel augmentation unit for classifying human behavior in various scenes, having a total number of trainable parameters of 5.3 m with an average inference time of 0.09 s. The model has been trained and evaluated on four public datasets: Real Live Violence Situations (RLVS), Highway Incident Detection (HWID), Movie Fights, and Hockey Fights. The proposed technique achieved accuracies in the range of 92% to 99.5% across the used benchmark datasets. Comprehensive analysis and comparisons between different versions of the model and the state-of-the-art have been performed to confirm the model’s performance in terms of accuracy and efficiency. The proposed model has higher accuracy with an average of 4.97%, and higher efficiency by reducing the number of parameters by around 139.1 m compared to other models trained and tested on the same benchmark datasets.

Typ des Eintrags: Artikel
Erschienen: 2024
Autor(en): Elmasry, Ramez M. ; Abd El Ghany, Mohamed A. ; Salem, Mohammed A.-M. ; Fahmy, Omar M.
Art des Eintrags: Zweitveröffentlichung
Titel: MultiWave-Net: An Optimized Spatiotemporal Network for Abnormal Action Recognition Using Wavelet-Based Channel Augmentation
Sprache: Englisch
Publikationsjahr: 7 Mai 2024
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 24 Januar 2024
Ort der Erstveröffentlichung: Basel
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: AI
Jahrgang/Volume einer Zeitschrift: 5
(Heft-)Nummer: 1
DOI: 10.26083/tuprints-00027246
URL / URN: https://tuprints.ulb.tu-darmstadt.de/27246
Zugehörige Links:
Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in this work, abnormal behavior is regarded as human aggression or in another context when car accidents occur on the road. As this behavior can negatively affect the surrounding traffic participants, such as vehicles and other pedestrians, it is crucial to monitor such behavior. Given the current prevalent spread of cameras everywhere with different types, they can be used to classify and monitor such behavior. Accordingly, this work proposes a new optimized model based on a novel integrated wavelet-based channel augmentation unit for classifying human behavior in various scenes, having a total number of trainable parameters of 5.3 m with an average inference time of 0.09 s. The model has been trained and evaluated on four public datasets: Real Live Violence Situations (RLVS), Highway Incident Detection (HWID), Movie Fights, and Hockey Fights. The proposed technique achieved accuracies in the range of 92% to 99.5% across the used benchmark datasets. Comprehensive analysis and comparisons between different versions of the model and the state-of-the-art have been performed to confirm the model’s performance in terms of accuracy and efficiency. The proposed model has higher accuracy with an average of 4.97%, and higher efficiency by reducing the number of parameters by around 139.1 m compared to other models trained and tested on the same benchmark datasets.

Freie Schlagworte: abnormal actions, anomaly, accidents, convolutional neural network, convolutional LSTM, channel augmentation, fights, recognition, wavelet transform, violence
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-272460
Zusätzliche Informationen:

This article belongs to the Special Issue Artificial Intelligence-Based Image Processing and Computer Vision

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Integrierte Elektronische Systeme (IES)
Hinterlegungsdatum: 07 Mai 2024 12:49
Letzte Änderung: 13 Mai 2024 08:13
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