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Comparison of Anomaly Detection Methods on Event-Based Vision Sensor Data in a High Noise Environment.

Will Johnston1, Anthony Franz2, Shannon Young2

  • 1Department of Engineering Physics, Air Force Institute of Technology, 2950 Hobson Way, Fairborn, OH 45433, USA.

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Summary
This summary is machine-generated.

Event-based vision sensors (EVSs) can detect targets even with strong interference. Principal Component Background Suppression (PCBS) and Mahalanobis Distance (MD) detectors show the best performance in high noise environments.

Keywords:
Mahalanobis distance detector distance detectorReed–Xiaoli detectoranomaly detectioncomplementary subspace detectordynamic vision sensorevent-based vision sensorfrequency analysishigh noise environmenthyperspectral analysisneuromorphic cameraprincipal component background suppression

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Sensor Technology

Background:

  • Event-based vision sensors (EVSs) offer high temporal resolution and unique event data output.
  • Anomaly detection in hyperspectral analysis can be adapted for EVS event frequency spectra.
  • Strong interfering sources can significantly reduce EVS sensitivity and obscure targets.

Purpose of the Study:

  • To compare five eigenanalysis anomaly detection methods for target detection in high noise EVS data.
  • To evaluate detector performance in the presence of overwhelming interfering sources.
  • To identify optimal detection methods for challenging EVS applications.

Main Methods:

  • Applied Principal Component Background Suppression (PCBS), Mahalanobis Distance (MD), Complementary Subspace Detector (CSD), Reed-Xiaoli (RX), and Subspace Reed-Xiaoli (SSRX) detectors.
  • Utilized frequency analysis, background suppression, and statistical filtering techniques.
  • Evaluated detection probability against false-alarm probability in simulated high noise conditions.

Main Results:

  • PCBS, MD, and CSD detectors successfully detected targets through strong interference.
  • PCBS demonstrated superior performance at low false-alarm rates (e.g., >400% detection increase at 10-5 false-alarm probability).
  • MD and CSD excelled at higher false-alarm rates (approx. 7 × 10-2), with MD offering sub-second execution.

Conclusions:

  • PCBS and MD detectors are recommended for target detection in high noise EVS environments.
  • The choice between PCBS and MD depends on the specific application's requirements for false-alarm rate and speed.
  • Eigenanalysis anomaly detection methods show promise for robust target identification with EVS technology.