Multi-Target analysis for the Blueband
Members: Minwoo Park, Yanxi Liu, Robert Collins
|
Project Descriptions
Near regular texture (NRT) is prevalent yet uncontrolled phenomenon.
This implies that the analysis of NRT can give more robust solution to many real world problems.
As a representative example, Lin and Liu [1] show the dynamic NRT configuration can track dynamic NRT robustly under the challenging situations while other methods fail. Because statistical dependency of NRT components can be well encoded into graphical model, they used Markov random field to model NRT and belief propagation (BP) for inference of graph configuration. For this reason, dynamic NRT cannot go without the graphical model and, thus good inference framework in the graph is one of the fundamental components of the dynamic NRT analysis. However, the current state-of-the-art inference solution in the graph, non-parametric BP, is still slow and inaccurate for large hidden variable space. To tackle this problem, we have developed efficient and accurate mean-shift belief propagation framework. To demonstrate the efficiency, accuracy and generality, we have tested our proposed method on multi-target tracking and a 2D articulated body tracking application and
show that the proposed method is better than other methods, namely BP and NBP, in terms of speed, stability and accuracy.
However for the complete analysis of the Blueband, several more algorithms are needed.
Publications
Related Links
