Autonomous UAV for Suspicious Action Detection using Pictorial Human Pose Estimation and Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual autonomous systems capable of monitoring crowded areas and alerting the authorities in occurrence of a suspicious action can play a vital role in controlling crime rate. Previous atte mpts have been made to monitor crime using posture recognition but nothing exclusive to investigating actions of people in large populated area has been cited. In order resolve this shortcoming, we propose an autonomous unmanned aerial vehicle (UAV) visual surveillance system that locates humans in image frames followed by pose estimation using weak constraints on position, appearance of body parts and image parsing. The estimated pose, represented as a pictorial structure, is flagged using the proposed Hough Orientation Calculator (HOC) on close resemblance with any pose in the suspicious action dataset. The robustness of the system is demonstrated on videos recorded using a UAV with no prior knowledge of background, lighting or location and scale of the human in the image. The system produces an accuracy of 71% and can also be applied on various other video sources such as CCTV camera.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it