A detection unit 41 of a counting device 40 executes a detection process to be counted from each of a plurality of frame images that constitute a captured video in which the movable bodies to be counted are included. A selection unit 42 selects, among the plurality of frame images constituting the captured video, a portion of the frame images as selected images such that the capturing order is intermittent and displays, in a display device, each of the selected images in an aspect where detection result information indicating the movable bodies to be counted detected by the detection process are superimposed on the selected image. A correction unit 43 receives, as correction information, information for correcting the result of the detection process displayed in each of the selected images. A counting unit 44 counts the movable bodies to be counted included in the captured video.

Disclosed are fibers which contain identification fibers. The identification fibers can contain a plurality of distinct features, or taggants, which vary among the fibers and/or along the length of the identification fibers, tow band, or yarn. The disclosed embodiments also relate to the method for making the fibers. Characterization of the fibers can include identifying distinct features, combinations of distinct features, and number of fibers with various combinations of distinct features and correlating the distinct features to supply chain information. The supply chain information can be used to track the fibers, fiber band, or yarn from manufacturing through intermediaries, conversion to final product, and/or the consumer.

Disclosed are fibers which contain identification fibers. The identification fibers can contain a plurality of distinct features, or taggants, which vary among the fibers and/or along the length of the identification fibers, tow band, or yarn. The disclosed embodiments also relate to the method for making the fibers. Characterization of the fibers can include identifying distinct features, combinations of distinct features, and number of fibers with various combinations of distinct features and correlating the distinct features to supply chain information. The supply chain information can be used to track the fibers, fiber band, or yarn from manufacturing through intermediaries, conversion to final product, and/or the consumer.

A sensor includes: a complex transfer function calculator calculating complex transfer functions; a living body component extractor extracting living body information; a correlation matrix calculator calculating a target correlation matrix from the living body information; a noise information storage recording a noise correlation matrix; a first headcount information calculator calculating first headcount information that is a tentative number of persons present in the predetermined space, based on the target correlation matrix and a threshold calculated from the noise correlation matrix; a MUSIC spectrum calculator estimating position candidates for the living bodies, using the target correlation matrix, and outputting likelihood spectra indicating likelihoods of the respective living bodies being present in corresponding positions; and a second headcount information calculator estimating second headcount information that is a more accurate number of living bodies or positions from position information that is based on the likelihood spectra and can include the position candidates.

A sensor includes: a complex transfer function calculator calculating complex transfer functions; a living body component extractor extracting living body information; a correlation matrix calculator calculating a target correlation matrix from the living body information; a noise information storage recording a noise correlation matrix; a first headcount information calculator calculating first headcount information that is a tentative number of persons present in the predetermined space, based on the target correlation matrix and a threshold calculated from the noise correlation matrix; a MUSIC spectrum calculator estimating position candidates for the living bodies, using the target correlation matrix, and outputting likelihood spectra indicating likelihoods of the respective living bodies being present in corresponding positions; and a second headcount information calculator estimating second headcount information that is a more accurate number of living bodies or positions from position information that is based on the likelihood spectra and can include the position candidates.

The present invention provides a same fish easy identification device, a fish counting device, a mobile terminal for counting fish, a same fish identification method, a fish counting method, a fish count prediction device, a fish count prediction method, a same fish identification system, a fish counting system, and a fish count prediction system. The same fish identification device of the present invention includes: a measurement image acquisition unit 11; a fish position information acquisition unit 131; a predicted fish position information acquisition unit 132; and a same fish identification unit 133. The measurement image acquisition unit 11 acquires, over time, n measurement images of a region to be measured in a passage region where a fluid containing fish passes through. The fish position information acquisition unit 131 acquires fish position information in the n measurement images. The predicted fish position information acquisition unit 132 selects a selected image(s) from the measurement images acquired prior to a (m1)th measurement image among the n measurement images and acquires predicted fish position information (PI.sub.m) in the m-th measurement image on the basis of the fish position information in the selected image. On the basis of the fish position information (I.sub.m) in the m-th measurement image and the corresponding predicted fish position information (PI.sub.m), the same fish identification unit 133 identifies the fish in the m-th measurement image as the same fish as in the selected image when the fish position information (I.sub.m) matches with the predicted fish position information (PI.sub.m) and identifies the fish in the m-th measurement image as not being the same fish as in the selected image when the fish position information (I.sub.m) does not match with the predicted fish position information (PI.sub.m).

A chip recognition system in which a chip is configured to at least partially have a specific color indicative of a value of the chip includes: a recording device that uses a camera and records a state of the chip as an image; an image analysis device that subjects the image so recorded to image analysis and recognizes at least the specific color and a reference color that is present in the image and differs from the specific color; and a recognition device at least including an artificial intelligence device that uses a result of the image analysis by the image analysis device and specifies the specific color of the chip, wherein the artificial intelligence device of the recognition device has been subjected to teaching using, as training data, a plurality of images of the chip and the reference color irradiated with different illumination intensities.

A chip recognition system in which a chip is configured to at least partially have a specific color indicative of a value of the chip includes: a recording device that uses a camera and records a state of the chip as an image; an image analysis device that subjects the image so recorded to image analysis and recognizes at least the specific color and a reference color that is present in the image and differs from the specific color; and a recognition device at least including an artificial intelligence device that uses a result of the image analysis by the image analysis device and specifies the specific color of the chip, wherein the artificial intelligence device of the recognition device has been subjected to teaching using, as training data, a plurality of images of the chip and the reference color irradiated with different illumination intensities.

Disclosed are fibers which contain identification fibers. The identification fibers can contain a plurality of distinct features, or taggants, which vary among the fibers and/or along the length of the identification fibers, tow band, or yarn. The disclosed embodiments also relate to the method for making the fibers. Characterization of the fibers can include identifying distinct features, combinations of distinct features, and number of fibers with various combinations of distinct features and correlating the distinct features to supply chain information. The supply chain information can be used to track the fibers, fiber band, or yarn from manufacturing through intermediaries, conversion to final product, and/or the consumer.

Disclosed are fibers which contain identification fibers. The identification fibers can contain a plurality of distinct features, or taggants, which vary among the fibers and/or along the length of the identification fibers, tow band, or yarn. The disclosed embodiments also relate to the method for making the fibers. Characterization of the fibers can include identifying distinct features, combinations of distinct features, and number of fibers with various combinations of distinct features and correlating the distinct features to supply chain information. The supply chain information can be used to track the fibers, fiber band, or yarn from manufacturing through intermediaries, conversion to final product, and/or the consumer.