Fall detection system based on real-time pose estimation and SVM
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
With the rapid growth of the elderly population, fall detection has become a key issue in the medical and health field. Accurately detecting fall behavior in surveillance video and timely feedback can effectively reduce the injury and even death of the elderly due to falls. For the complex scenes in surveillance video and the interference of multiple similar human behaviors, this paper proposes a method based on pose estimation and the auxiliary detection method based on yoloV5. First, extract video frames from different falling video sequences to form a data set; then, input the training sample set into the improved network for training until the network converges; finally, test the category of the target in the video according to the optimized network model and locate the target. Experimental results show that the improved algorithm can effectively detect falls or Activities of Daily Living (ADL) events in each frame of the image and give real-time feedback. The detection of falling behavior in the video further verifies the feasibility and efficiency of the recognition method based on our deep learning methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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