Modeling Pedestrian Behavior and Detecting Event Anomalies using a Seasonal Kalman Filter

Abstract

We present a seasonal state-space model using Kalman recursions to learn and predict structured behavior patterns. The model is employed to detect events using the learned expectations of typical scene activity. We demonstrate the approach for modeling the expected number of pedestrians in a scene over hour-long periods (over multiple days) and for detecting event anomalies. The framework provides a single long-term model by exploiting the natural seasonal trends in daily human activity.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
AD1001143

Entities

People

  • James W. Davis
  • Mark A. Keck

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Bayesian Networks
  • Change Detection
  • Computer Science
  • Data Science
  • Decomposition
  • Detection
  • Detectors
  • Engineering
  • Equations
  • Filters
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Statistical Algorithms
  • Surveillance

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation

Technology Areas

  • Space
  • Space - Space Objects