An information processing apparatus comprising: a computation unit that analyzes a sensing result acquired by sensing a vehicle running on a road, and thereby computing the number of occupants in the vehicle by: computing, for each seat of the vehicle, in a case in which it is determined that a person exists on the seat, a reliability of the determination, and computing a certainty factor related to each case of the possible number of the occupants in the vehicle using the reliability for each seat; a selection unit that selects the sensing result with the computed certainty factor of the number of occupants which does not satisfy a predetermined criterion as an analysis result; and a processing unit that highlights the sensing result selected by the selection unit with a specific mark or a specific color, wherein the computation unit computes the reliability for each seat by acquiring a plurality of images by capturing using one imaging apparatus or by capturing from different angles by two or more imaging apparatuses, wherein the computation unit computes the reliability for each seat using Bayesian belief network, BBN, wherein the computation unit converts a likelihood distribution acquired from the following equation into the certainty factor using Bayes' theorem and a prior probability p(n) that is computed in advance based on statistics of the numbers of occupants in vehicles passing through a location in which the sensing takes place, [Math 1] p x →, n ′, θ → | n = exp − α n 2 n ′ − n 2 + β n 2 ∑ k = 1 n γ n, k 2 x k 1 + ∑ k = n + 1 N γ n, k 2 x k wherein n is a true value of the number of occupants in the vehicle, n' is the number of occupants in the vehicle computed by the computation unit, x (an arrow representing a vector is omitted) is the reliability for each seat, θ (an arrow representing a vector is omitted) = (α i, β i, γ i,j) (1 ≤ i, j ≤ N) is a model parameter and a constant, and N is the maximum number of occupants in the vehicle. a computation unit that analyzes a sensing result acquired by sensing a vehicle running on a road, and thereby computing the number of occupants in the vehicle by: computing, for each seat of the vehicle, in a case in which it is determined that a person exists on the seat, a reliability of the determination, and computing a certainty factor related to each case of the possible number of the occupants in the vehicle using the reliability for each seat; computing, for each seat of the vehicle, in a case in which it is determined that a person exists on the seat, a reliability of the determination, and computing a certainty factor related to each case of the possible number of the occupants in the vehicle using the reliability for each seat; a selection unit that selects the sensing result with the computed certainty factor of the number of occupants which does not satisfy a predetermined criterion as an analysis result; and a processing unit that highlights the sensing result selected by the selection unit with a specific mark or a specific color, wherein the computation unit computes the reliability for each seat by acquiring a plurality of images by capturing using one imaging apparatus or by capturing from different angles by two or more imaging apparatuses, wherein the computation unit computes the reliability for each seat using Bayesian belief network, BBN, wherein the computation unit converts a likelihood distribution acquired from the following equation into the certainty factor using Bayes' theorem and a prior probability p(n) that is computed in advance based on statistics of the numbers of occupants in vehicles passing through a location in which the sensing takes place, [Math 1] p x →, n ′, θ → | n = exp − α n 2 n ′ − n 2 + β n 2 ∑ k = 1 n γ n, k 2 x k 1 + ∑ k = n + 1 N γ n, k 2 x k wherein n is a true value of the number of occupants in the vehicle, n' is the number of occupants in the vehicle computed by the computation unit, x (an arrow representing a vector is omitted) is the reliability for each seat, θ (an arrow representing a vector is omitted) = (α i, β i, γ i,j) (1 ≤ i, j ≤ N) is a model parameter and a constant, and N is the maximum number of occupants in the vehicle.