Direction Estimation of Pedestrian from Images

Abstract

The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots. We introduce an approach for estimating the walking direction of people from images, based on learning the correct classification of a still image by using SVMs. We find that the performance of the system can be improved by classifying each image of a walking sequence and combining the outputs of the classifier. Experiments were performed to evaluate our system and estimate the trade-off between number of images in walking sequences and performance.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2003
Accession Number
ADA459729

Entities

People

  • Hiroaki Simizu
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bodies
  • Classification
  • Cognitive Science
  • Computer Vision
  • Contracts
  • Human Body
  • Images
  • Kernel Functions
  • Learning
  • Machine Learning
  • Military Research
  • Recognition
  • Sequences
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks
  • Autonomy