Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

Ahmad, Tariq; Wu, Jinsong; Alwageed, Hathal Salamah; Khan, Faheem; Khan, Jawad; Lee, Youngmoon

doi:10.1109/access.2023.3263155

Cited by 21 publications

(8 citation statements)

References 49 publications

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“…The residual network has a strong feature learning ability and adapts to the characteristics of the backbone convolutional network architecture. In this study, ResNet-50 is adopted as the backbone network to solve the network degradation problem brought by fewer samples of abstract paintings to simplify the model training parameters of this article to a certain extent, improve the training efficiency, and carry out comparative experiments with the residual network variant in the ablation experiments, and to assess the influence of the backbone network on the model accuracy rate (Ahmad et al, 2023 ). The abstract painting dataset is generated by the backbone network to generate canonical image features with a length and width of 7 and a channel count of 256 and is spread into a one-dimensional sequence, resulting in an image sequence of length 49 and a channel count of 256 to be fed to the encoder.…”

Section: Attention Givenmentioning

confidence: 99%

Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism

Cheng

2024

Front. Comput. Neurosci.

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Existing methods for classifying image emotions often overlook the subjective impact emotions evoke in observers, focusing primarily on emotion categories. However, this approach falls short in meeting practical needs as it neglects the nuanced emotional responses captured within an image. This study proposes a novel approach employing the weighted closest neighbor algorithm to predict the discrete distribution of emotion in abstract paintings. Initially, emotional features are extracted from the images and assigned varying K-values. Subsequently, an encoder-decoder architecture is utilized to derive sentiment features from abstract paintings, augmented by a pre-trained model to enhance classification model generalization and convergence speed. By incorporating a blank attention mechanism into the decoder and integrating it with the encoder's output sequence, the semantics of abstract painting images are learned, facilitating precise and sensible emotional understanding. Experimental results demonstrate that the classification algorithm, utilizing the attention mechanism, achieves a higher accuracy of 80.7% compared to current methods. This innovative approach successfully addresses the intricate challenge of discerning emotions in abstract paintings, underscoring the significance of considering subjective emotional responses in image classification. The integration of advanced techniques such as weighted closest neighbor algorithm and attention mechanisms holds promise for enhancing the comprehension and classification of emotional content in visual art.

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Section: Attention Givenmentioning

confidence: 99%

Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism

Cheng

2024

Front. Comput. Neurosci.

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“…The efficiency of the proposed approach is due to the fused feature extraction and classification performance. https://www.indjst.org/ (40) UCF101 dataset 93.30 Implicit CNN (41) UCF101 dataset 89.8 GMM-KF-GRNN (42) UCF101 dataset 89.30 AR3D (43) UCF101 dataset 89.28 S3D-ConvNet (44) UCF101 dataset 86.6 Encoding RNNs (45) UCF101 dataset 81.9 P-RRNNs (46) UCF101 dataset 91.4 Deep Bi-LSTM (47) UCF101 dataset 91.21 CNN_Bi-GRU (48) UCF101 dataset 91.79…”

Section: Performance Analysismentioning

confidence: 99%

“…Table 7 indicates the comparative analysis of the proposed HAR model with different DL algorithms. The proposed accuracy outcome is compared with other DL models like Two-stream ConvNet (40) , Implicit CNN (41) , Gaussian Mixture Model-Kalman Filter-Gated RNN (GMM-KF-GRNN) (42) , Attention Residual 3D (AR3D) Network (43) , Segments based 3D ConvNet (S3D-ConvNet) (44) , Encoding RNNs (45) , P-RRNNs (46) , Deep Bi-LSTM (47) , and CNN_Bi-GRU (48) . The same UCF101 dataset is used by the existing DL models for accuracy performance comparison.…”

Section: Performance Analysismentioning

confidence: 99%

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Shah,

Holia

2024

IJST

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Background/Objectives: The accurate recognition of human activities from video sequences is very challenging due to low resolution, cluttered background, partial occlusion, and different viewpoints. Machine learning (ML) based automated HAR from surveillance videos is required with the fusion of various feature extraction techniques. Methods: In this paper, SVM with feature fusion is utilized for automatic recognition from surveillance videos. A Histogram of Oriented Gradient (HOG) is used to segment the frame to differentiate humans from other objects or background noise in the input video frames. The multi-feature extraction can be accomplished in terms of Gabor Wavelet Transform (GWT), Autocorrelogram, Gray-Level Co-Occurrence Matrix (GLCM), HSV histogram, and Multi-dimensional CNN. The proposed approach is implemented in MATLAB software and compared with existing approaches like Space-Time Interest Point (STIP) and Histogram of Optical Flow (HOF). Findings: The proposed approach outperforms the existing approaches in terms of reduced time consumption and high accuracy, 99.886% when using the UCF101 dataset and 99.538% when using the UTKinect dataset. Novelty: The most discriminative feature information is obtained with the feature-level fusion technique. From the feature information, various human actions are recognized with the classification algorithm. Keywords: Human activity recognition, Machine Learning, Surveillance Videos, Human detection algorithm, Feature extraction, SVM classifier

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“…In paper [21] they developed a privacy-protecting face recognition system via federated learning, in which facial recognition models were trained collaboratively without sharing raw data. They secured accurate face recognition while keeping users' identities secret [38][39][40][41]. One popular approach proposed automatically segmenting and labeling single-channel or multimodal biosignal ta using a self-similarity matrix (SSM) computed with signals' feature-based representations [42].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Rahim

Zhong

Ahmad

et al. 2023

Sensors

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Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

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Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

Cited by 21 publications

References 49 publications

Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism

Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

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