An Pedestrian Trajectory Anomaly Detection for Python.
Project description
ewha_anomaly_detection
A Python package for detecting abnormal pedestrian trajectory with LSTM-VAE model.
How to use
'''python from ewha_anomaly_detection.anomaly_detection import Anomaly_Detection as ad
weights_path = "/your_directory/vae_lstm_weights.weights.h5"
anmly = ad(df=df, weights_path=weights_path) anomaly_1, anomaly_2 = anmly.call() '''
ad.call() automatically predicts (1) the abnormal trajectory IDs from your pedestrian trajectory dataset and (2) the abnormal trajectory ratio for each CCTV road segment from which the trajectories were extracted.
Requirements
numpy == 1.24.3 pandas geopandas shapely tensorflow
License
This project is licensed under the JuyeonCho License.
Contact
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file ewha_anomaly_detection-0.1.3.tar.gz.
File metadata
- Download URL: ewha_anomaly_detection-0.1.3.tar.gz
- Upload date:
- Size: 4.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a989ff0569f946ff11ad4b34179b07ae38a10054a7a48746fcd0a66ec6522b87
|
|
| MD5 |
ba811096c6fd72ce62e4280d08675faf
|
|
| BLAKE2b-256 |
6c53a8640fc69d2ff987e112323cf4e472557fedd6d8fd5bbb868ae60f5e03e9
|
File details
Details for the file ewha_anomaly_detection-0.1.3-py3-none-any.whl.
File metadata
- Download URL: ewha_anomaly_detection-0.1.3-py3-none-any.whl
- Upload date:
- Size: 4.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
306bcbe71201da6d6eee69cf939abab76e6ff4dbec6c0fdfa3a92e7c0876dbe2
|
|
| MD5 |
06bb74a8640d3a12f77286eb5593d27b
|
|
| BLAKE2b-256 |
555caed210c2fb33bf51eaf7a4d44b0a074265aa65b1d9afbba4262ae7bec7c2
|
