Abnormal Behavior Detection in Gait Analysis Using Convolutional Neural Networks
Bibliographic record
Abstract
The focus of this study encompasses the burgeoning field of abnormal behavior detection through computer vision, with a specific emphasis on gait analysis.A foundational gait model has been constructed, deriving from an extensive analysis of various gait types.The research endeavors to establish a model capable of discerning individual abnormal behavior, predicated on their walking patterns.A meticulous evaluation and comparison of three predominant feature extraction methodologies-Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Center Symmetric Local Binary Pattern (CS-LBP)-constitute the core of this study.These techniques have been selected owing to their prevalent application and validated efficacy across numerous computer vision domains.Following feature extraction, the classification stage is initiated, utilizing Convolutional Neural Networks (CNNs), a paradigm of deep learning algorithms.The methodology has undergone rigorous testing and evaluation on a comprehensive dataset, inclusive of both standard and aberrant behavioral instances.A high performance level, signified by a 99% accuracy rate, was achieved through the application of the CS-LBP method for abnormal behavior detection.The empirical results underscore the significance of gait feature extraction methods in augmenting the system's proficiency in anomaly detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".