Joint Probabilistic Data Association Revisited
2015 IEEE International Conference on Computer Vision (ICCV)(2015)
摘要
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
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关键词
joint probabilistic data association,JPDA technique,integer linear program,clutter density,global tracking method
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