Skip to main content

An Pedestrian Trajectory Anomaly Detection for Python.

Project description

ewha_anomaly_dynamicP

A Python package for detecting abnormal pedestrian trajectory with LSTM-VAE model. Specifically, for dynamic points table of a Ian DataBase

How to use

'''python from ewha_anomaly_dynamicP.anomaly_dynamicP 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

whwndus13@naver.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ewha_anomaly_dynamicp-0.1.0.tar.gz (4.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ewha_anomaly_dynamicp-0.1.0-py3-none-any.whl (4.9 kB view details)

Uploaded Python 3

File details

Details for the file ewha_anomaly_dynamicp-0.1.0.tar.gz.

File metadata

  • Download URL: ewha_anomaly_dynamicp-0.1.0.tar.gz
  • Upload date:
  • Size: 4.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for ewha_anomaly_dynamicp-0.1.0.tar.gz
Algorithm Hash digest
SHA256 494d44acd2d134d368a86b445d29a84ebb72c681facea8b6fe8063a1bcea6b78
MD5 4865968a3d47fe0d7fe09236ff8626ef
BLAKE2b-256 a6cb8d499d23fea52e10b53f19d768f93ab067582c5f5f85a35449a23b2999d5

See more details on using hashes here.

File details

Details for the file ewha_anomaly_dynamicp-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for ewha_anomaly_dynamicp-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a16f2b493c6f1cdf848314f0e79e4409d394bdad05efcf83dfed7f8b2688473e
MD5 2c24faf60b1bfbbd0be4f0359e4e3b26
BLAKE2b-256 d6ab33536c53375217bff295ead12513c14af16a6db885feb2ff467233eed2f3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page