Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compression of motion capture databases
ACM SIGGRAPH 2006 Papers
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Synthesis and evaluation of linear motion transitions
ACM Transactions on Graphics (TOG)
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
This paper presents a novel approach to tracking articulated human motion with monocular video. In a conventional tracking system based on particle filters, it is very challenging to track a complex human pose with many degrees of freedom. A typical solution to this problem is to track the pose in a low dimensional latent space by manifold learning techniques, e.g., the Gaussian process dynamical model (GPDM model). In this paper, we extend the GPDM model into a graph structure (called GPDM graph) to better express the diverse dynamics of human motion, where multiple latent spaces are constructed and dynamically connected to each other appropriately by an unsupervised learning method. Basically, the proposed model has both intra-transitions (in each latent space) and inter-transitions (among latent spaces). Moreover, the probability of inter-transition is dynamic, depending on the current latent state. Using the proposed GPDM graph model, we can track human motion with monocular video, where the average tracking errors are improved from the state-of-the-art methods in our experiments.