Illumination invariant sequential filtering human tracking
Many tracking problems can be efficiently solved by the Altering technique. Linear filter methods (e.g. Kaiman Filter) have shown their success and optimally in many linear settings with Gaussian noises. However, they expose inefficiency and weakness in the general nonlinear and high dimensional setting (e.g. human tracking). While, the advancement of Sequential Importance Re-sampling with Simulated Annealing has shown it is capable of handling nonlinearity and high dimensionality of human...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC 2010)|
|01_Lu_Illumination_invariant_2010.pdf||619.01 kB||Adobe PDF||Request a copy|
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