Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition
Bibliographic record
Abstract
Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".