Information processing device, control method, and program

Abstract

An information processing apparatus ( 2000 ) generates likelihood data for each of a plurality of partial regions ( 12 ) in image data ( 10 ). The likelihood data are data being associated with a position and a size on the image data ( 10 ) and indicating a likelihood that a target object exists in an image region at the position with the size. The information processing apparatus ( 2000 ) computes a distribution (probability hypothesis density: PHD) of an existence likelihood of a target object with respect to a position and a size by computing the total sum of likelihood data each piece of which is generated for each partial region ( 12 ). The information processing apparatus ( 2000 ) extracts, from the PHD, partial distributions each of which relates to one target object. For each extracted partial distribution, the information processing apparatus ( 2000 ) outputs a position and a size of a target object represented by the partial distribution, based on a statistic of the partial distribution.

1 . An information processing apparatus comprising:
 at least one memory configured to store instructions; and   at least one processor configured to execute the instructions to perform:
 training a neural network by use of one or more combinations of prepared learning image data and an ideal PHD for each of mutually different types of target objects; 
 acquiring image data; 
 generating likelihood data for each of a plurality of partial regions included in the image data by inputting the acquired image data to the trained neural network; 
 computing a distribution of a likelihood of existence of the target objects with respect to a position and a size by computing a total sum of the likelihood data, and extracting, from the computed distribution, one or more partial distributions each of which relates to one target object; and 
 outputting, for each of the one or more partial distribution, a position and a size of the one target object relating to the partial distribution, based on a statistic of the partial distribution. 
   
     
     
         2 . The information processing apparatus according to  claim 1 , wherein
 the likelihood data is represented by a distribution conforming to a predetermined model, and   for the each partial region, the trained neural network outputs a likelihood that a target object exists in the partial region and a parameter value of the predetermined model.   
     
     
         3 . The information processing apparatus according to  claim 1 , wherein
 the at least one processor is configured to execute the instructions to perform:
 computing a number of target objects included in the image data, based on an integral value of the distribution represented by the total sum of the likelihood data, and 
 extracting as many as the number of the partial distributions from the distribution represented by the total sum of the likelihood data. 
   
     
     
         4 . The information processing apparatus according to  claim 1 , wherein
 the at least one processor is configured to execute the instructions to perform:
 extracting the partial distributions an integral value of each of which is 1 from the distribution represented by the total sum of the likelihood data. 
   
     
     
         5 . The information processing apparatus according to  claim 1 , wherein
 the at least one processor is configured to execute the instructions to perform:
 generating the likelihood data for each of mutually different types of the target objects; 
 computing, for each of mutually different types of the target objects, a distribution of a likelihood of existence of the target objects and extracting the partial distribution from the distribution; and 
 outputting a position and a size of a target object relating to the each partial distribution along with a type of the target objects relating to the partial distribution. 
   
     
     
         6 . A control method executed by at least one computer, the control method comprising:
 training a neural network by use of one or more combinations of prepared learning image data and an ideal PHD for each of mutually different types of target objects;   acquiring image data;   generating likelihood data for each of a plurality of partial regions included in the image data by inputting the acquired image data to the trained neural network;   computing a distribution of a likelihood of existence of the target objects with respect to a position and a size by computing a total sum of the likelihood data, and extracting, from the computed distribution, one or more partial distributions each of which relates to one target object; and   outputting, for each of the one or more partial distribution, a position and a size of the one target object relating to the partial distribution, based on a statistic of the partial distribution.   
     
     
         7 . The control method according to  claim 6 , wherein,
 the control method comprises:
 the likelihood data is represented by a distribution conforming to a predetermined model, and 
 for the each partial region, the trained neural network outputs a likelihood that a target object exists in the partial region and a parameter value of the predetermined model. 
   
     
     
         8 . The control method according to  claim 6 , wherein
 the control method comprises:
 computing a number of target objects included in the image data, based on an integral value of the distribution represented by the total sum of the likelihood data; and 
 extracting as many as the number of the partial distributions from a distribution represented by the total sum of the likelihood data. 
   
     
     
         9 . The control method according to  claim 6 , wherein
 the control method comprises:
 extracting the partial distributions an integral value of each of which is 1 from a distribution represented by the total sum of the likelihood data. 
   
     
     
         10 . The control method according to  claim 6 , wherein
 the control method comprises:
 generating the likelihood data for each of mutually different types of the target objects; 
 computing, for each of mutually different types of the target objects, a distribution of a likelihood of existence of the target objects and extracting the partial distribution from the distribution; and 
 outputting a position and a size of a target object relating to the each partial distribution along with a type of the target objects relating to the partial distribution. 
   
     
     
         11 . A non-transitory recording medium storing a program causing at least one computer to execute:
 training a neural network by use of one or more combinations of prepared learning image data and an ideal PHD for each of mutually different types of target objects;   acquiring image data;   generating likelihood data for each of a plurality of partial regions included in the image data by inputting the acquired image data to the trained neural network;   computing a distribution of a likelihood of existence of the target objects with respect to a position and a size by computing a total sum of the likelihood data, and extracting, from the computed distribution, one or more partial distributions each of which relates to one target object; and   outputting, for each of the one or more partial distribution, a position and a size of the one target object relating to the partial distribution, based on a statistic of the partial distribution.