Real-time decentralized articulated motion analysis and object tracking from videos.
In this paper, we present two new articulated motion analysis and object tracking approaches: the decentralized articulated object tracking method and the hierarchical articulated object tracking method. The first approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking. Instead, we introduce a decentralized scheme and model the interpart interaction within an innovative Bayesian framework. Specifically, we estimate the interaction density by an efficient decomposed interpart interaction model. To handle severe self-occlusions, we further extend the first approach by modeling high-level interunit interaction and develop the second algorithm within a consistent hierarchical framework. Preliminary experimental results have demonstrated the superior performance of the proposed approaches on real-world videos in both robustness and speed compared with other articulated object tracking methods.
Qu
Wei
W
Motorola Labs, Schaumburg, IL 60196, USA. wei.qu@motorola.com
Schonfeld
Dan
D
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena / Q000379:methods*; D003199:Computer Systems; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D007596:Joints / Q000033:anatomy & histology / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*