Pedestrian Validation in Infrared Images by Means of Active Contours and Neural Networks

Abstract

This paper presents two different modules for the validation of human shape presence in far-infrared images. These modules are part of a more complex system aimed at the detection of pedestrians by means of the simultaneous use of two stereo vision systems in both far-infrared and daylight domains. The first module detects the presence of a human shape in a list of areas of attention using active contours to detect the object shape and evaluating the results by means of a neural network. The second validation subsystem directly exploits a neural network for each area of attention in the far-infrared images and produces a list of votes.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA523342

Entities

People

  • Massimo Bertozzi
  • Michael Del Rose
  • Mirko Felisa
  • Pietro Cerri
  • Stefano Ghidoni

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Cameras
  • Complex Systems
  • Computer Stereo Vision
  • Computer Vision
  • Daylight
  • Detection
  • Detectors
  • Images
  • Information Processing
  • Infrared Images
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Signal Processing
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Sensor Fusion and Tracking Systems.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks