%0 Conference Paper %1 torma2003a %A Torma, P. %A Szepesvári, Cs. %B 2003 IEEE International Symposium on Intelligent Signal Processing %D 2003 %K application filtering, particle theory, vision, %T Combining Local Search, Neural Networks and Particle Filters to Achieve Fast and Reliable Contour Tracking %X LS-N-IPS is an extension of the standard N-IPS particle filter (also known as CONDENSATION in the image processing literature). The modified algorithm adds local search to the baseline algorithm: in each time step the predictions are refined in a local search procedure that utilizes the most recent observation. A critical choice in the design of LS-N-IPS is the way the local search is implemented. Here, we introduce a method based on training artificial neural networks for implementing the local search. In experiments with real-life data (visual tracking) the method is shown to improve robustness and performance significantly, surpassing the performance of previous state-of-the-art algorithms.

@inproceedings{torma2003a, abstract = {LS-N-IPS is an extension of the standard N-IPS particle filter (also known as CONDENSATION in the image processing literature). The modified algorithm adds local search to the baseline algorithm: in each time step the predictions are refined in a local search procedure that utilizes the most recent observation. A critical choice in the design of LS-N-IPS is the way the local search is implemented. Here, we introduce a method based on training artificial neural networks for implementing the local search. In experiments with real-life data (visual tracking) the method is shown to improve robustness and performance significantly, surpassing the performance of previous state-of-the-art algorithms.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Torma, P. and Szepesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/272b1fb414bc545a98ddb220c1686f809/csaba}, booktitle = {2003 IEEE International Symposium on Intelligent Signal Processing}, date-modified = {2010-09-02 13:09:15 -0600}, interhash = {8335548fa72c0b3176876cec527dd5e1}, intrahash = {72b1fb414bc545a98ddb220c1686f809}, keywords = {application filtering, particle theory, vision,}, owner = {Beata}, pdf = {papers/lsnipsneuro.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Combining Local Search, Neural Networks and Particle Filters to Achieve Fast and Reliable Contour Tracking}, year = 2003 }