State estimation apparatus, state estimation method, and integrated circuit

A state estimation method for estimating an internal state of an observation target, the state estimation method comprising:

• obtaining first observation data from an observable event at predetermined time intervals;
• obtaining second observation data from the observable event at predetermined time intervals;
• obtaining possibility measurement data by performing addition processing or logical OR processing with the first observation data and the second observation data;
• obtaining necessity measurement data by performing multiplication processing or logical AND processing with the first observation data and the second observation data;
• performing prediction using posterior probability distribution data, which is probability distribution, obtained at a preceding time t−1, of an internal state of the observation target to obtain predictive probability distribution data, which is probability distribution of an internal state of the observation target at a current t;
• obtaining a first likelihood based on the predictive probability distribution data and the possibility measurement data and obtaining a second likelihood based on the predictive probability distribution data and the necessity measurement data;
• estimating, from the first likelihood and the predictive probability distribution data, posterior probability distribution data, which is probability distribution of the internal state of the observation target at the current time t;
• and
• outputting prior probability distribution data based on the posterior probability distribution data estimated by the step of estimating posterior probability distribution data as prior probability distribution data at a next time t+1 in order to detect and track the observation target in a moving image.

An integrated circuit for estimating an internal state of an observation target, the integrated circuit comprising:

• first observation obtaining circuitry configured to obtain first observation data from an observable event at predetermined time intervals;
• second observation obtaining circuitry configured to obtain second observation data from the observable event at predetermined time intervals;
• possibility measurement obtaining circuitry configured to obtain possibility measurement data by performing addition processing or logical OR processing with the first observation data and the second observation data;
• necessity measurement obtaining circuitry configured to obtain necessity measurement data by performing multiplication processing or logical AND processing with the first observation data and the second observation data;
• prediction circuitry configured to perform prediction using posterior probability distribution data, which is probability distribution, obtained at a preceding time t−1, of an internal state of the observation target to obtain predictive probability distribution data, which is probability distribution of an internal state of the observation target at a current time t;
• likelihood obtaining circuitry configured to obtain a first likelihood based on the predictive probability distribution data and the possibility measurement data and to obtain a second likelihood based on the predictive probability distribution data and the necessity measurement data;
• posterior probability distribution estimation circuitry configured to estimate, from the first likelihood and the predictive probability distribution data, posterior probability distribution data, which is probability distribution of the internal state of the observation target at the current time t;
• and
• prior probability distribution output circuitry configured to output prior probability distribution data based on the posterior probability distribution data estimated by the posterior probability distribution estimation circuitry as prior probability distribution data at a next time t+1 in order to detect and track the observation target in a moving image.