2D Silhouette and 3D Skeletal Models for Human Detection and Tracking

Authors:
Carlos Orrite-Urunuela;Jesus Martinez del Rincon;J. Elias Herrero-Jaraba;Gregory Rogez
Affiliations:
University of Zaragoza, Spain;University of Zaragoza, Spain;University of Zaragoza, Spain;University of Zaragoza, Spain
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

Citing 0
Cited 9

Vision-based human motion analysis: An overview

Computer Vision and Image Understanding
A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

Pattern Recognition
Dealing with Non-linearity in Shape Modelling of Articulated Objects

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Performance analysis for gait in camera networks

AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Performance analysis for automated gait extraction and recognition in multi-camera surveillance

Multimedia Tools and Applications
A conditional entropy-based independent component analysis for applications in human detection and tracking

EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Skin colour segmentation based 2D and 3D human pose modelling using Discrete Wavelet Transform

Pattern Recognition and Image Analysis
Human figure segmentation using independent component analysis

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
SMI 2012: Full Skeleton pruning by contour approximation and the integer medial axis transform

Computers and Graphics

Quantified Score

Hi-index	0.00

Visualization

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.