Title :
2D silhouette and 3D skeletal models for human detection and tracking
Author :
Orrite-Uruñuela, Carlos ; Del Rincón, Jesù s Martínez ; Herrero-Jaraba, J. Elías ; Rogez, Grégory
Author_Institution :
Aragon Inst. for Eng. Res., Zaragoza Univ., Spain
Abstract :
In This work 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-linear 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 helps increase the reliability and robustness.
Keywords :
gait analysis; image motion analysis; image sequences; object detection; pattern classification; principal component analysis; 2D silhouette model; 3D skeletal model; gait sequences; human detection; human gait analysis; nearest neighbor classifier; point distribution model approach; principal component analysis; Biological system modeling; Computer vision; Deformable models; Electronic mail; Humans; Mathematical model; Nearest neighbor searches; Performance analysis; Principal component analysis; Shape;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1333749