Abstract: Among multiple different area classes designated regarding objects appearing in a captured image that has been acquired, to which area class each pixel in the captured image belongs is recognized. An unrecognized-class area not belonging to any of the multiple area classes in an area in the captured image representing a prescribed area class among the multiple different area classes is identified as an area-of-interest candidate. A determination is made regarding whether the area-of-interest candidate is a prescribed desired area.

Abstract: An image processing device includes a reception interface and a processor. The reception interface receives image data corresponding to an image in which a subject is captured. The processor detects, based on the image data, a left shoulder feature point and a right shoulder feature point of the person. The processor estimates a hidden body part of the person that is not captured in the image due to obstruction by another body part of the person based on a distance between the left shoulder feature point and the right shoulder feature point.

Abstract: An image processing device includes a reception interface and a processor. The reception interface is configured to receive image data corresponding to an image in which a subject is captured. The processor is configured to perform, with respect to the image data, processing that a skeleton model in which a plurality of feature points corresponding to four limbs are connected to a center feature point corresponding to a center of a human body is applied to the subject.

Abstract: An image processing device includes a reception interface and a processor. The reception interface receives image data corresponding to an image in which a subject is captured. The processor detects, based on the image data, a left shoulder feature point, a right shoulder feature point, and a face feature point of the person. The processor acquires a first value corresponding to a distance between the left shoulder feature point and the face feature point. The processor acquires a second value corresponding to a distance between the right shoulder feature point and the face feature point. The processor estimates presence or absence of a body twist of the person based on a ratio between the first value and the second value.

Abstract: A reception interface receives image data corresponding to an image in which a person is captured. In a case where at least one of feature points belonging to a left upper limb group is estimated to be included in a hidden body part and at least one of the feature points belonging to a right upper limb group is estimated to be included in the hidden body part, one of all the feature points belonging to the left upper limb group and all the feature points belonging to the right upper limb group are handled as feature points included in the hidden body part, and the other one of all the first feature points and all the second feature points are handled as feature points included in a non-hidden body part.

Abstract: A mobile entity detection apparatus 10 includes: a feature point extraction unit 11 configured to extract, from image data output by an imaging device of a vehicle, a feature point moving in an image; an optical flow specification unit 12 configured to compare the latest image data with image data that was output in the past, and specify an optical flow for each feature point; a clustering unit 13 configured to perform clustering on the optical flow based on the optical flow and the position of the optical flow in the image, and separate the optical flow into a cluster; a filtering unit 14 configured to remove a cluster that corresponds to an object forming a background from among the clusters based on the traveling direction and speed of the vehicle; and a mobile entity specification unit 15 configured to specify a mobile entity in the image using a result of clustering after cluster removal.

Abstract: A mobile entity detection apparatus 10 includes: a feature point extraction unit 11 configured to extract, from image data output by an imaging device of a vehicle, a feature point moving in an image; an optical flow specification unit 12 configured to compare the latest image data with image data that was output in the past, and specify an optical flow for each feature point; a clustering unit 13 configured to perform clustering on the optical flow based on the optical flow and the position of the optical flow in the image, and separate the optical flow into a cluster; a filtering unit 14 configured to remove a cluster that corresponds to an object forming a background from among the clusters based on the traveling direction and speed of the vehicle; and a mobile entity specification unit 15 configured to specify a mobile entity in the image using a result of clustering after cluster removal.