Intelligent Human Anomaly Identification and Classification in Crowded Scenes via Multi-fused Features and Restricted Boltzmann Machines
Bibliographic record
Abstract
With the advancement of technology and the breakthroughs of the modern era, accessing and transforming that data into information is becoming a more complex task for the scientific community. Specifically, a wide range of wearable and vision sensors are employed to capture multimodal data from diverse sources and fields. These have been incorporated into numerous domains and applications to assess academic and remote systems, emergency personnel, and monitoring systems. This paper presents a robust human anomaly detection and classification method in crowded scenes. First, crowdsourced data is acquired as an input. A few normalizing and filtering steps for denoising are performed. Then, human silhouettes are abstracted, which significantly facilitates human detection. Then, crowd-based analysis and clustering are employed for precise and efficient predictions. Following that feature engineering process, three robust features are extracted, including deep flow, gradient patches, and dense optical flow-based descriptors. Furthermore, stochastic gradient descent (SGD) was utilized for feature selection and optimization. Finally, optimized features are further fed to the Restricted Boltzmann Machines (RBM) classifier to advance adaptive training for the classification and predictions of human behavior in crowded scenes. The experimental results revealed an 88.1% accuracy and a 12.36 % error rate for the Avenue dataset. The ADOC dataset attained an average recognition rate of 91.17 percent, and an error rate of 8.82 percent. Finally, the USCD-Ped 2 dataset achieved an improved recognition rate of 90.19% with an error rate of 9.81%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".