EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Fast Stereo-Based Head Tracking for Interactive Environments
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Online selection of the best k-feature subset for object tracking
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses.