Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
Joint audio-visual tracking using particle filters
EURASIP Journal on Applied Signal Processing
Adaptive uncertainty estimation for particle filter-based trackers
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Visual detection and target tracking are interdisciplinary tasks oriented to estimate the state of moving objects in an image sequence. There are different techniques focused on this problem. It is worth highlighting particle filters and Kalman filters as two of the most important tracking algorithms in the literature. In this paper, we presented a visual tracking algorithm which combines the particle filter framework with memory strategies to handle occlusions, called as memory-based particle filter (MbPF). The proposed algorithm follows the classical particle filter stages when a confidence measurement can be obtained from the system. Otherwise, a memory-based module try to estimate the hidden target state and to predict its future states using the process history. Experimental results showed that the performance of the MbPF is better than a standard particle filter when dealing with occlusion situations.