Nec Corporation

Devendra Dhaka

Atsushi Sato

Masato Ishii

The information processing apparatus () of the example embodiment 1 includes an acquisition unit (), a modeling unit (), an output unit (). The acquisition unit () acquires a plurality of trajectory data. The trajectory data represents a time-sequence of observed positions of an object. The modeling unit () assigns one of groups for each trajectory data. The modeling unit () generates a generative model for each group. The generative model represents trajectories assigned to the corresponding group by a common time-sequence of velocity transformations. The velocity transformation represents a transformation of velocity of the object from a previous time frame, and is represented using a set of motion primitives defined in common for all groups. The output unit () outputs the generated generative models.

Information processing apparatus, and control method

The information processing apparatus () of the example embodiment 1 includes an acquisition unit (), a clustering unit (), and a modeling unit (). The acquisition unit () acquires a plurality of trajectory data. Until a predetermined termination condition is satisfied, the clustering unit () repeatedly performs: 1) dividing the plurality of trajectory data into one or more groups using a group identity distribution of each trajectory data; 2) determining a time-sequence of representative velocity for each group; 3) determining, for each trajectory data, a time-sequence of a latent position distribution of a corresponding object; and 4) determining a scaling factor for each trajectory data; and 5) updating the group identity distribution of each trajectory data. The modeling unit () generates a model data for each group. The model data includes the time-sequence of representative velocity generated by the clustering unit ().

Information processing apparatus, method, and non-transitory computer-readable medium

The information processing apparatus () of the example embodiment 1 includes an acquisition unit (), a clustering unit (), a transformation unit () and modeling unit (). Until a predetermined termination condition is determined, the clustering unit () repeatedly preforms: 1) optimizing the posterior parameters for clustering assignment for each data streams; 2) optimizes the posterior parameters for each determined cluster and for each time frame; 3) optimizes the posterior parameters for individual responses for each data stream; 4) optimizes the posterior parameters for latent states, via approximating the observation model through non-conjugate inference. The transformation unit () transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit () generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit ().

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Tokyo, Japan

Devendra Dhaka