A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes

Deep learning base solutions for computer vision made life easier for humans. Video data contain a lot of hidden information and patterns, that can be used for Human Action Recognition (HAR). HAR can apply to many areas, such as behavior analysis, intelligent video surveillance, and robotic vision....

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Vydané v:	The Journal of supercomputing Ročník 78; číslo 2; s. 2873 - 2908
Hlavní autori:	Bilal, Muhammad, Maqsood, Muazzam, Yasmin, Sadaf, Hasan, Najam Ul, Rho, Seungmin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.02.2022 Springer Nature B.V
Predmet:	Artificial Intelligence based Deep Video Data Analytics Compilers Computer Science Computer vision Data transmission Deep learning Feature extraction Human activity recognition Human motion Interpreters Iterative methods Machine learning Machine vision Occlusion Processor Architectures Programming Languages Surveillance Video data Human action recognition Transfer learning Overlapping actions Video content analysis Deep autoencoder
ISSN:	0920-8542, 1573-0484
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Abstract	Deep learning base solutions for computer vision made life easier for humans. Video data contain a lot of hidden information and patterns, that can be used for Human Action Recognition (HAR). HAR can apply to many areas, such as behavior analysis, intelligent video surveillance, and robotic vision. Occlusion, viewpoint variation, and illumination are some issues that make the HAR task more difficult. Some action classes have similar actions or some overlapping parts in them. This, among many other problems, is the main reason that contributes the most to misclassification. Traditional hand-engineering and machine learning-based solutions lack the ability to handle overlapping actions. In this paper, we propose a deep learning-based spatiotemporal HAR framework for overlapping human actions in long videos. Transfer learning techniques are used for deep feature extraction. Fine-tuned pre-trained CNN models learn the spatial relationship at the frame level. An optimized Deep Autoencoder was used to squeeze high-dimensional deep features. An RNN with LSTM was used to learn the long-term temporal relationships. An iterative module added at the end to fine-tune the trained model on new videos that learns and adopt changes. Our proposed framework achieved state-of-the-art performance in spatiotemporal HAR for overlapping human actions in long visual data streams for non-stationary surveillance environments.
AbstractList	Deep learning base solutions for computer vision made life easier for humans. Video data contain a lot of hidden information and patterns, that can be used for Human Action Recognition (HAR). HAR can apply to many areas, such as behavior analysis, intelligent video surveillance, and robotic vision. Occlusion, viewpoint variation, and illumination are some issues that make the HAR task more difficult. Some action classes have similar actions or some overlapping parts in them. This, among many other problems, is the main reason that contributes the most to misclassification. Traditional hand-engineering and machine learning-based solutions lack the ability to handle overlapping actions. In this paper, we propose a deep learning-based spatiotemporal HAR framework for overlapping human actions in long videos. Transfer learning techniques are used for deep feature extraction. Fine-tuned pre-trained CNN models learn the spatial relationship at the frame level. An optimized Deep Autoencoder was used to squeeze high-dimensional deep features. An RNN with LSTM was used to learn the long-term temporal relationships. An iterative module added at the end to fine-tune the trained model on new videos that learns and adopt changes. Our proposed framework achieved state-of-the-art performance in spatiotemporal HAR for overlapping human actions in long visual data streams for non-stationary surveillance environments.
Author	Hasan, Najam Ul Bilal, Muhammad Rho, Seungmin Yasmin, Sadaf Maqsood, Muazzam
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Keywords	Human action recognition Transfer learning Overlapping actions Video content analysis Deep autoencoder
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References_xml	– reference: Caetano CA, De Melo VHC, dos Santos JA, Schwartz WR (2017) Activity recognition based on a magnitude-orientation stream network. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), IEEE, pp 47–54 – reference: Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4489–4497 – reference: MuhammadKHussainTBaikSWEfficient CNN based summarization of surveillance videos for resource-constrained devicesPattern Recogn Lett202013037037510.1016/j.patrec.2018.08.003 – reference: Cai Z, Wang L, Peng X, Qiao Y (2014) Multi-view super vector for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 596–603 – reference: Zhu Y, Lan Z, Newsam S, Hauptmann A (2018) Hidden two-stream convolutional networks for action recognition. 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SubjectTerms	Artificial Intelligence based Deep Video Data Analytics Compilers Computer Science Computer vision Data transmission Deep learning Feature extraction Human activity recognition Human motion Interpreters Iterative methods Machine learning Machine vision Occlusion Processor Architectures Programming Languages Surveillance Video data
Title	A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes
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