Abnormal Human Activity Recognition and Classification Using Knowledge Graphs
Bibliographic record
Abstract
Intelligent verbal and action annotations have played an important role in railway stations, bus stands, police stations and road traffic to avoid crime diffusion, attacks and identify unhealthy human behavior.To predict and classify the human abnormal activities, which is occur at a remote location and/or unusual time by identify the movements from surveillance cameras for identifying the physical and verbal abnormal activities such as abuse, assault, arson, arrest, and fighting and raise an automatic alarm or notification.From the existing gap, an automation tool is required to detect the violence behavior with potential hazards and to prevent from crime to promote public safety.The proposed human activity and annotation recognition (HAAR) algorithm addresses the existing limitations by using contextual semantic analysis for identification of crowd, tracking of crowd behavior in large gatherings and people's abnormal behavior.It uses CNN and LSTM model to categorize the abnormal activity and generate a pattern by using contextual knowledge graph to identify the severity.HAAR model uses contextual knowledge graph with contextual vertices and edges to store human's verbal and physical action and objects involved.The captured attributes and its dependencies between the activities in a knowledge graph recognize the level of unusual behavior and generate ALERT messages to the authorities.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".