Patterns of motion in non-overlapping networks using vehicle tracking data

[摘要] (cont.) After we model the correspondence probability for observations captured by different source/sinks, we adopt a probabilistic framework to use this correspondence probability in a principled manner. Tracks are assigned by estimating the correspondences which maximize the posterior probabilities (MAP) using the Hungarian algorithm. After establishing the correspondence, we have a set of stitched trajectories, in which elements from each camera can be combined with observations in multiple subsequent cameras generated by the same object. Finally, we show how to learn the activity clusters and detect abnormal activities using the mixture of unigram model with the stitched trajectories as input. We adopt a bag - of - words presentation, and present a Bayesian probabilistic approach in which trajectories are represented by a mixture model. This model can classify trajectories into different activity clusters, and gives representations of both new trajectories and abnormal trajectories.

[发布机构] Massachusetts Institute of Technology