The Switching Linear Dynamical System (SLDS) can represent alternating pedestrian dynamics which transitions between walking and standing states. For online pedestrian path prediction from a moving vehicle, the SLDS is first extended with latent variables to exploit contextual information on where and when motion state transitions occur. Then, unsupervised learning of dynamics together with spatial context from unlabeled track data is considered. A spatial likelihood term is directly added to the SLDS states, such that these now capture dynamics for a specific spatial regions. This unsupervised approach is further extended into a hierarchical model to additionally cluster tracks into behavior classes.
Finally, these unsupervised techniques are applied to the data association problem in tracking itself, focusing on anomalous surveillance situations where object detections are inaccurate and motion models unreliable.
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