Optical Flow Analysis and Kalman Filter Tracking in Video Surveillance Algorithms

Abstract

A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was monitored by a modification of the traditional Kalman Filter. The Kalman Filter algorithm implemented has the ability to track up to ten contacts at a time correctly assigning each of a maximum ten filters to their respective contacts on a frame-by-frame basis. Initial tests using artificial data show good performance of both the Optical Flow Analysis algorithm and the Kalman Filter Tracking algorithm. Surveillance video data was also used to test the algorithm with promising results.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA473398

Entities

People

  • David A. Semko

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Artificial Intelligence
  • California
  • Change Detection
  • Detection
  • Electrical Engineering
  • Engineering
  • Filters
  • Filtration
  • Kalman Filters
  • Measurement
  • Sequences
  • Surveillance
  • Two Dimensional
  • Video Frames
  • Video Surveillance

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Sensor Fusion and Tracking Systems.