Enhanced Abnormal Activity Detection: Utilizing YOLOv8 and Deep SORT with TSAI and LSTM Classifiers
Bibliographic record
Abstract
Video analysis has undergone a radical transformation thanks to the quick development of deep learning and computer vision techniques, especially in the area of anomalous activity detection. Robust abnormal activity recognition algorithms are a prerequisite for strong security and safety protocols, which has led to the investigation of novel approaches. This work examined the combination of two state-of-the-art classifiers—Long Short-Term Memory, or LSTM, and Time Series AI, or TSAI—with cutting-edge techniques for object tracking and recognition, such as YOLOv8 (You Only Look Once, version 8) and Deep SORT (Simple Online and real-time tracking). The suggested method attempted to precisely identify anomalous activities in video feeds by utilizing the skills of LSTM and TSAI in capturing temporal dependencies and analyzing sequential data, along with the real-time object tracking abilities of Deep SORT and accurate object detection of YOLOv8. The performance of the integrated system was evaluated utilizing precision, recall, and F1-score metrics during an extensive examination that covered a variety of datasets and situations, resulting in an astounding 97.22% accuracy. With the ultimate goal of improving the system’s practical usefulness in real-world settings across industries like retail, disaster management, healthcare, and building security, the study also investigated the effects of various configurations and parameters on the system’s efficacy. By improving video analysis techniques, this research helps enterprises improve security protocols and successfully maintain public safety.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".