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Real-time probabilistic data association over streams

The Probabilistic Data Association (PDA) problem involves identifying correspondences between items over data sequences on the basis of similarity functions. PDA has long been a topic of interest in many application areas such as real-time tracking and surveillance. Despite its significance, however, it has largely been ignored by the event-processing community. Our work rectifies this situation by studying PDA in the context of continuous event processing.
 Specifically, we formulate PDA as a continuous probabilistic ranking problem with constraints and efficiently solve it using fast constraint resolution. Our solutions are built on a top-k approximation to the problem guided by resource-aware optimization techniques that adaptively utilize the available resources to produce real-time results. User-defined data association constraints are used to restrict the solution space and quickly eliminate inconsistent solution candidates. We also derive the runtime complexity of our solutions and experimentally evaluate these solutions using a prime PDA application: real-time tracking of moving objects within a camera network. Our evaluation results demonstrate the superiority of our solutions over traditional constraint programming formulations.