A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Pictorial Structures for Object Recognition
International Journal of Computer Vision
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
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Estimating Gait Phase using Low-Level Motion
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
International Journal of Computer Vision
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In this work a method is presented to track and estimate pose of articulated objects using the motion of a sparse set of moving features. This is achieved by using a bottom-up generative approach based on the Pictorial Structures representation [1]. However, unlike previous approaches that rely on appearance, our method is entirely dependent on motion. Initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as Pictorial Structures using motion. A standard feature tracker is used to automatically extract a sparse set of features. These features typically contain many tracking errors, however, the presented approach is able to overcome both this and their sparsity. The proposed method is applied to two problems: 2D pose estimation of articulated objects walking side onto the camera and 3D pose estimation of humans walking and jogging at arbitrary orientations to the camera. In each domain quantitative results are reported that improve on state of the art. The motivation of this work is to illustrate the information present in low-level motion that can be exploited for the task of pose estimation.