Human Fall Detection Based on ResNet and LSTM Network in Surveillance Cameras
Bibliographic record
Abstract
One of the biggest problems with an elderly living alone at home is that of falls. Without prompt assistance and medical treatment, failure to detect falls in a timely manner could lead to more serious injury or death. While there are existing solutions that requires the elderly to wear sensors such as accelerometers or gyroscopes on their bodies, this is inconvenient to them and many will resist putting on such sensors. There is therefore a need to look for a less intrusive solution such as using computer vision and deep learning. In this paper, we proposed a novel framework which combines the advantages of Residual neural network (ResNet) and Long-Short term memories network (LSTM) to perform fall detection. The feature maps can be extracted from each frames by the trained ResNet network which is used to identify important features. Those feature maps are then fed in to an LSTM model to detect the existence of a fall. Our approach achieved a 100% detection accuracy on the URFD dataset and a 97.3% detection accuracy on the FDD dataset, outperforming other state-of-the-art algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".