People Recognition in Image Sequences by Supervised Learning

Abstract

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classi- ers (SVMs). Di erent types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classi ers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADA459706

Entities

People

  • Bernd Heisele
  • Chikahito Nakajima
  • Massimiliano Pontil
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cameras
  • Change Detection
  • Cognitive Science
  • Convolution
  • Data Sets
  • Detection
  • Graphs
  • Histograms
  • Learning
  • Recognition
  • Sequences
  • Supervised Machine Learning
  • Surveillance
  • Test Sets
  • Training
  • Visual Surveillance

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML