Human Action Recognition Using Squeezed Convolutional Neural Network

Nasaoui, Hakim; Bellamine, Insaf; Satori, Hassan

doi:10.1109/isivc54825.2022.9800210

Cited by 2 publications

(2 citation statements)

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“…Among the comparative methods, the lowest accuracy on the UCF11 dataset was obtained by the Local-global features + QSVM method [32], which achieved an accuracy of 82.6%. The rest of the comparative methods included Multi-task hierarchical clustering [33], BT-LSTM [34], Deep autoencoder [35], Two-stream attention LSTM [36], Weighted entropy-variances-based feature selection [37], Dilated CNN + BiLSTM + RB [38], DS-GRU [39], Squeezed CNN [40], BS-2SCN [41], and 3DCNN [42]. These methods achieved accuracies of 89.7%, 85.3%, 96.2%, 96.9%, 94.5%, 89.0%, 97.1%, 87.4%, 90.1%, and 85.1%, respectively.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Multi-task hierarchical clustering [33] 2017 89.7 BT-LSTM [34] 2018 85.3 Deep autoencoder [35] 2019 96.2 Two-stream attention LSTM [36] 2020 96.9 Weighted entropy-variances based feature selection [37] 2021 94.5 Dilated CNN+BiLSTM+RB [38] 2021 89.0 DS-GRU [39] 2021 97.1 Local-global features + QSVM [32] 2021 82.6 Squeezed CNN [40] 2022 87.4 Fusion-based discriminative features [31] 2022 97.8 BS-2SCN [41] 2022 90.1 3DCNN [42] 2022 85.1 DA-R3DCNN (Proposed) 2023 98.6…”

Section: Methods Year Accuracy (%)mentioning

confidence: 99%

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Human Action Representation Learning Using an Attention-Driven Residual 3DCNN Network

Ullah,

Munir

2023

Algorithms

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The recognition of human activities using vision-based techniques has become a crucial research field in video analytics. Over the last decade, there have been numerous advancements in deep learning algorithms aimed at accurately detecting complex human actions in video streams. While these algorithms have demonstrated impressive performance in activity recognition, they often exhibit a bias towards either model performance or computational efficiency. This biased trade-off between robustness and efficiency poses challenges when addressing complex human activity recognition problems. To address this issue, this paper presents a computationally efficient yet robust approach, exploiting saliency-aware spatial and temporal features for human action recognition in videos. To achieve effective representation of human actions, we propose an efficient approach called the dual-attentional Residual 3D Convolutional Neural Network (DA-R3DCNN). Our proposed method utilizes a unified channel-spatial attention mechanism, allowing it to efficiently extract significant human-centric features from video frames. By combining dual channel-spatial attention layers with residual 3D convolution layers, the network becomes more discerning in capturing spatial receptive fields containing objects within the feature maps. To assess the effectiveness and robustness of our proposed method, we have conducted extensive experiments on four well-established benchmark datasets for human action recognition. The quantitative results obtained validate the efficiency of our method, showcasing significant improvements in accuracy of up to 11% as compared to state-of-the-art human action recognition methods. Additionally, our evaluation of inference time reveals that the proposed method achieves up to a 74× improvement in frames per second (FPS) compared to existing approaches, thus showing the suitability and effectiveness of the proposed DA-R3DCNN for real-time human activity recognition.

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