2D silhouette and 3D skeletal models for human detection and tracking

C Orrite-Urunuela, J M del Rincon, J E Herrero-Jaraba, G Rogez

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

23 Citations (Scopus)

Abstract

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.

Original languageEnglish
Title of host publicationPROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4
EditorsJ Kittler, M Petrou, M Nixon
Place of PublicationLOS ALAMITOS
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages244-247
Number of pages4
Volume4
ISBN (Print)0-7695-2128-2
Publication statusPublished - 2004
Event17th International Conference on Pattern Recognition (ICPR) - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Conference

Conference17th International Conference on Pattern Recognition (ICPR)
CountryUnited Kingdom
CityCambridge
Period23/08/200426/08/2004

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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