Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework

Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown...

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Bibliographic Details
Published in:Journal of imaging Vol. 9; no. 7; p. 130
Main Authors: Ullah, Hayat, Munir, Arslan
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 26.06.2023
MDPI
Subjects:
Accuracy
activity recognition
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
channel–spatial attention
Computational efficiency
Computing time
convolutional neural network
Data mining
Datasets
Deep learning
Effectiveness
Feature maps
Frames (data processing)
Frames per second
gated recurrent unit
Hands
Human activity recognition
Human acts
Human behavior
Internet of Things
Machine learning
Methods
Model accuracy
Neural networks
pattern recognition
Robustness (mathematics)
Surveillance
Video data
United States
deep learning
gated recurrent unit
convolutional neural network
activity recognition
channel–spatial attention
pattern recognition
ISSN:2313-433X, 2313-433X
Online Access:Get full text
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Summary:Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial–temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel–spatial attention mechanism to extract human-centric salient features in video frames. The dual channel–spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging9070130