Abnormal Event Detection and Localization Based on Crowd Analysis in Video Surveillance
Bibliographic record
Abstract
With the rapid development of economy and urban construction, abnormal events detection has arouse spread attention in need of public security. When an abnormal event occurs, the pedestrians in crowd escape or run instinctively which will lead to sharp change in the collectiveness feature and kinetic energy of crowd. This paper proposes a method based on the Collectiveness Energy Index (CEI) which combines the two features mentioned above to detect the abnormal events because it is not unreliable to utilize either of the two features singly. Besides, this paper also presents a means to locate abnormal behaviours in the anomalous scenes. Firstly, we obtain spatial coordinate of particles existed on individuals in each frame using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Then, the Collectiveness Energy Index (CEI) of each frame is calculated and compared with an adaptive threshold for abnormal events identification. In order to locate abnormal behaviours, this paper splits each input frame of video sequences into blocks without overlapping and then calculates the velocity and individual collectiveness of each block for classifying it as anomalous or not. Experiments conducted on UMN dataset and UCSD dataset verify the effectiveness and superiority of our detection and localization method.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".