Probabilistic pedestrian tracking based on a skeleton model

Jumpei Ashida, Ryusuke Miyamoto, Hiroshi Tsutsui, Takao Onoye, Yukihiro Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

A novel pedestrian tracking scheme based on a particle filter is proposed, which adopts a skeleton model of a pedestrian as a state space model and uses distance transformed images for likelihood estimation. The six-stick skeleton model used in the proposed approach is very distinctive in representing a pedestrian simply but effectively, with which the efficient state space for the pedestrian tracking can be derived. Experimental results by using PETS sample sequences demonstrate that the proposed approach achieves highly accurate pedestrian tracking without any of prior learning.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2825-2828
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: 8 Oct 200611 Oct 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period8/10/0611/10/06

Keywords

  • Bayes procedures
  • Image processing
  • Tracking

Cite this

Ashida, J., Miyamoto, R., Tsutsui, H., Onoye, T., & Nakamura, Y. (2006). Probabilistic pedestrian tracking based on a skeleton model. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 2825-2828). [4107157] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.312996