HumanFallDetection reduces a fall to five pose features and an LSTM verdict

Two classes, not a detector over pixels

The pipeline starts from human pose estimation rather than from video. Poses come from the openpifpaf library, and the project's stated contribution is augmenting that with support for multi-camera and multi-person tracking, then feeding the result to a long short-term memory network.

What the LSTM consumes is five features, described as temporal and spatial, extracted from the poses. The output is one of two classes, Fall or No Fall. That shape matters: the model is not a detector in the object detection sense, and it does not localise a person or draw a box. It is a sequence classifier sitting on top of a pose estimate, which is why the video argument documentation talks about compiling files into videos and why the training data is a recorded fall dataset.

The classification boundary is what you would want to inspect first in any use of this code, because everything upstream exists to make that one bit more reliable.

Camera count and a filename pattern you must guess

The whole interface is one script, `python3 fall_detector.py`, plus four arguments:

python3 fall_detector.py

The camera count is the first, defaulting to 1, and it tells the detector how many cameras or videos are in play. Passing `None` for the video path switches from files to live capture from the camera.

The filename handling is the roughest part of the interface. For a single video, the documentation says to save your videos as `abc.xyz` and set `--video=abc.xyz`. For two videos it says to save them as `abc1.xyz` and `abc2.xyz` and then set `--video=abc.xyz`, which is a single path argument describing two files. The detector is therefore inferring the second filename from a pattern rather than accepting a list, and anyone with real filenames will have to rename files to fit it.

Saving the processed result is opt-in through `save_output`, which defaults to False, so by default the script displays work and writes nothing.

Output video lands beside the input with an out prefix

When `save_output` is enabled, the result is written as a video file, and the location rule is fixed: output videos go in the same directory as the input videos, with out appended at the start of the title. The flag defaults to False.

A prefix rule like this is convenient when the input directory is under your control and annoying when it is not. Processing a folder that already holds previous outputs means the naming has to be reasoned about in advance, since nothing in the argument list selects an output directory or a filename.

The repository layout is consistent with the pipeline being split into small pieces. At the top level there is `fall_detector.py` as the entry point, `algorithms.py`, `helpers.py`, `default_params.py`, `process_data.py`, a `model/` directory, a `vis/` directory for visualisation, and `examples/`, which holds a single recorded clip, `fall.gif`.

disable_cuda is an escape hatch, not a speed control

The fourth argument, `disable_cuda`, processes frames on the CPU by disabling CUDA support on the GPU, and it also defaults to False. In other words, GPU acceleration is on unless you turn it off, and there is no argument for choosing a device, limiting frame rate, or selecting a smaller model.

That is the whole configuration surface. There is no confidence threshold to tune, no target class list, no batch size, and no way to ask the script for per-clip scores through the documented arguments. A classifier that answers with one of two labels and no published threshold leaves calibration to whoever runs it.

The published paper behind the work claims real-time fall detection in its title, along with multi-camera and multi-person capability. The repository itself does not publish frame rates, accuracy, or false alarm figures, so the timing claim has to be read from the paper rather than from this code.

One dataset, and a notebook to prepare it

The LSTM was trained on the UP-Fall Detection dataset, and the repository links to it rather than redistributing it. A Colab notebook is provided to download the dataset and compile the files into videos, which is the step that turns raw recordings into the input format the detector expects.

Keeping the data out of the repository is the right default for a recorded fall dataset, but it means the training path is not reproducible from a clone alone. You need the dataset, the notebook, and the preprocessing step in `process_data.py` before you can retrain anything, and the weights in `model/` are the only trained artifact provided.

For a two-class problem trained on a single dataset, the interesting question for any reuse is how the classifier behaves on fall types that the training set does not contain, and this repository does not answer that.

One dependency pinned exactly, everything else a floor

Installation is one command, `pip install -r requirements.txt`, and the requirements file is worth reading before you run it:

pip install -r requirements.txt

The scientific stack is stated as minimums: torch at 1.6.0 or newer, torchvision at 0.7.0 or newer, openpifpaf at 0.11.7 or newer, numpy at 1.19.1 or newer, matplotlib at 3.3.1 or newer, pandas at 1.1.1 or newer, and Pillow at 7.2.0 or newer, alongside opencv-python and scipy.

One entry behaves differently. urllib3 is pinned to exactly 1.26.19 rather than given a floor, which is the sort of constraint that will either save an environment or collide with it.

The remainder of the file lists what look like resolved transitive dependencies, from certifi, chardet, idna, and requests through dataclasses, future, six, pytz, python-dateutil, and pysparkling. Installing the file as written therefore installs a flat set rather than letting a resolver choose.

MIT licensed, master branch, no releases

The repository is MIT licensed and sits on the master branch, and it asks that a specific paper be cited if the work helps your research. That paper is Multi-camera, multi-person, and real-time fall detection using long short term memory, authored by Mohammad Taufeeque, Samad Koita, Nicolai Spicher, and Thomas M. Deserno, published in the SPIE volume Medical Imaging 2021, volume 11601, pages 35 to 42, with a DOI of 10.1117/12.2580700.

For the state of the code itself, the signals are thin. There are no GitHub releases and no tagged versions to compare, and the last push to master is dated 2026-04-29. The code is a research artifact rather than a maintained package, so the dependency floors above should be read as the minimum it was built against rather than as a supported range, and the pinned urllib3 in particular is the line that will decide whether the file installs cleanly in a current environment.

Editorial conclusion

HumanFallDetection suits a researcher who wants a readable reference implementation of fall classification on top of pose estimation, with a clear feature extraction step and a small argument surface. It does not suit a deployment that needs calibration figures, a threshold sweep, or a statement about false alarm rates, none of which the repository publishes. Before running it, check that your input filenames follow the expected pattern, decide whether you need the saved output video, and read the pinned urllib3 version against the rest of your environment.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. README
4. taufeeque9/HumanFallDetection on GitHub