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BRAD: Bandwidth Reduction with Anomaly Detection

Modern compression techniques leverage a learned representation over an image distribution to more efficiently encode redundant spatial context for a sample. Video content is also generally redundant in time.

This work implements online training of convolutional autoencoders to learn a nonstationary image distribution in streaming video. Then, we perform anomaly detection by thresholding the autoencoder's reconstruction loss, which we regard as an anomaly score.

anomaly_detected

Setup

Adapt the included config.ini to suit your needs.

The demo uses OpenCV so you can configure the video sources with:

  • a webcam index (0,1 or /dev/video0)
  • an RTSP url
  • other URIs recognized by cv2.VideoCapture() (.mp4 or .mkv containers)

To affect model recency bias, consider changing:

  • the model learning rate (smaller rates --> less recency bias)
  • the time window length (smaller windows --> greater recency bias)

To explore different network architectures, change the net_arch parameter. This should be a list of 3-tuples organized as:

  • First entry - No. of Filters (Output dim)
  • Second entry - Size of Convolutional Kernel (assumed square)
  • Third entry - Size of Pooling (assumed square)

Dependencies

This repo requires opencv and tensorflow:

pip install -r requirements.txt

Running the Demo

Set the config to reflect your video sources and model preferences before running:

python3 visual_anomaly_detection_demo.py

A network is instantiated for each video source according to preferences set in the config.ini.

Online training begins and normal/anomaly classes along with the reconstruction loss will be streamed to stdout according to the configured threshold (by default 5 sigma deviation from moving average).

References