Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), and a modeling unit (2060). The acquisition unit (2020) acquires a plurality of trajectory data. Until a predetermined termination condition is satisfied, the clustering unit (2040) 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 (2060) generates a model data for each group. The model data includes the time-sequence of representative velocity generated by the clustering unit (2040).

Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), a transformation unit (2060) and modeling unit (2080). Until a predetermined termination condition is determined, the clustering unit (2040) 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 (2060) transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit (2060) generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit (2040).

Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a modeling unit (2040), an output unit (2080). The acquisition unit (2020) acquires a plurality of trajectory data. The trajectory data represents a time-sequence of observed positions of an object. The modeling unit (2040) assigns one of groups for each trajectory data. The modeling unit (2040) 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 (2060) outputs the generated generative models.

Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), a transformation unit (2060) and modeling unit (2080). Until a predetermined termination condition is determined, the clustering unit (2040) 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 (2060) transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit (2060) generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit (2040).

Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), and a modeling unit (2060). The acquisition unit (2020) acquires a plurality of trajectory data. Until a predetermined termination condition is satisfied, the clustering unit (2040) 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 (2060) generates a model data for each group. The model data includes the time-sequence of representative velocity generated by the clustering unit (2040).