A Probabilistic Framework for Combining Tracking Algorithms

For the past few years researches have been investigating enhancing tracking performance by combining several different tracking algorithms. We propose an analytically justified, probabilistic framework to combine multiple tracking algorithms. The separate tracking algorithms considered output a probability distribution function of the tracked state, sequentially for each image. The algorithms may output either an explicit probability distribution function, or a sample-set of it via CONDENSATION. The proposed framework is general and allows the combination of any set of separate tracking algorithms of this kind, even on different state spaces of different dimensionality, under a few reasonable assumptions. The combination may consist of different tracking algorithms that track a common object, as well as algorithms that track separate, albeit related objects, thus improving the tracking performance of each object. In many of the investigated settings, our approach allows us to treat t...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Ido Leichter, Michael Lindenbaum, Ehud Rivlin
• Keywords: Algorithms Considered Output, Computer Vision, CVPR 2004, Multiple Tracking Algorithms, Probability Distribution Function, Separate Tracking Algorithms, Tracking Performance