Abstract: In an object detection device, a plurality of object detection units output a score indicating the probability that a predetermined object exists for each partial region set with respect to inputted image data. On the basis of the image data, a weight computation unit uses weight computation parameters to compute weights for each of the plurality of object detection units, the weights being used when the scores outputted by the plurality of object detection units are merged. A merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. A loss computation unit computes a difference between a ground truth label of the image data and the scores merged by the merging unit as a loss. Then, a parameter correction unit corrects the weight computation parameters so as to reduce the computed loss.

Abstract: Learning means 701 learns a model for identifying an object indicated by data by using training data. First identification means 702 identifies the object indicated by the data by using the model learned by the learning means 701. Second identification means 703 identifies the object indicated by the data as an identification target used by the first identification means 702 by using a model different from the model learned by the learning means 701. The learning means 701 re-learns the model by using the training data including the label for the data determined based on the identification result derived by the second identification means 703 and the data.

Abstract: The present invention is a two-wheel vehicle riding person number determination system including an imaging means configured to image a two-wheel vehicle that is installed in a predetermined position and travels on a road, and a two-wheel vehicle riding person number determining means configured to process an image of the imaging means, extract a contour shape of an upper position of the two-wheel vehicle that travels on the road, detect humped shapes corresponding to heads of persons who ride on the two-wheel vehicle from the contour shape of the upper position of the two-wheel vehicle, and determine, on the basis of the humped shapes, whether or not the number of the persons who ride on the two-wheel vehicle is at least two persons or more.

Abstract: A selecting unit selects first to third moving image data from a plurality of frame images composing moving image data. A first generating unit generates first training data that is labeled data relating to a specific recognition target from the first moving image data. A first learning unit learns a first model recognizing the specific recognition target by using the first training data. A second generating unit generates second training data from the second moving image data by using the first model. A second learning unit learns a second model by using the second training data. A third generating unit generates third training data from the third moving image data by using the second model.

Abstract: A selecting unit selects first moving image data and second moving image data from a plurality of frame images composing moving image data. A first generating unit generates first training data that is labeled data relating to a specific recognition target from the frame images composing the first moving image data. A learning unit learns a first model recognizing the specific recognition target by using the first training data. A second generating unit generates second training data that is labeled data relating to the specific recognition target from the frame images composing the second moving image data by using the first model.

Abstract: The present disclosure provides an image processing apparatus capable of efficiently generating a learning model having a high accuracy. An image processing apparatus (1) includes a data acquisition unit (2), a data generation unit (4), a recognition accuracy calculation unit (6), and a learning data output unit (8). The data acquisition unit (2) acquires input image data. The data generation unit (4) converts the input image data by using a data conversion parameter and newly generates image data. The recognition accuracy calculation unit (6) calculates a recognition accuracy of the image data generated by the data generation unit (4) by using a learning model stored in advance. The learning data output unit (8) outputs, as learning data, the image data of which the recognition accuracy calculated by the recognition accuracy calculation unit (6) is lower than a first threshold.

Abstract: A three-dimensional space generating unit 81 generates a three-dimensional space modeling a three-dimensional model with associated attributes and a first background in a virtual space. A two-dimensional object drawing unit 82 draws a two-dimensional object by projecting the three-dimensional model in the three-dimensional space onto a two-dimensional plane. A label generating unit 83 generates a label from the attributes associated with the three-dimensional model from which the two-dimensional object is projected. A background synthesizing unit 84 generates a two-dimensional image by synthesizing the two-dimensional object and a second background. A training data generating unit 85 generates training data that associates the two-dimensional image in which the second background and the two-dimensional object are synthesized with the generated label.

Abstract: In order to fly a flying body more safely using a flight route according to a fall enable area, there is provided an image processing apparatus including a detector that detects a fall enable area on the ground surface based on a flying body video obtained when a flying body captures a ground area spreading below while moving. The image processing apparatus further includes a falling range estimator that estimates a falling range on the ground surface in case of a fall, and a decision unit that decides a flight route of the flying body such that the falling range is covered by the detected fall enable area.

Abstract: To more reliably land a flying body at a desired point, a flying body includes a determiner that determines whether the flying body is taking off and ascending from a takeoff point or descending to land, a camera mounted in the flying body, a recorder that records a lower image captured by the camera if it is determined that the flying body is taking off and ascending and a guider that, if it is determined that the flying body is descending to land, guides the flying body to the takeoff point while descending using a lower image recorded in the recorder during takeoff/ascent and a lower image captured during the descent.

Abstract: To accurately detect theft of a product. An information processing apparatus (2000) acquires a reference image (13) in which a product exhibition location is imaged. In addition, the information processing apparatus (2000) acquires a comparison image (14), in which a product shelf is imaged at a time after the reference image (13) is imaged. Furthermore, the information processing apparatus (2000) computes the quantity of reduction in products inside a surveillance area (15) included in both the reference image (13) and the comparison image (14). Furthermore, the information processing apparatus (2000) outputs the warning in a case where the computed quantity of reduction is equal to or larger than the reference value.

Abstract: Identification means 71 identifies an object indicated by data by applying the data to a model learned by machine learning. Determination means 72 determines whether or not the data is transmission target data to be transmitted to a predetermined computer based on a result obtained by applying the data to the model. Data transmission means 73 transmits the data determined to be the transmission target data to the predetermined computer at a predetermined timing.

Abstract: Learning means 701 learns a model for identifying an object indicated by data by using training data. First identification means 702 identifies the object indicated by the data by using the model learned by the learning means 701. Second identification means 703 identifies the object indicated by the data as an identification target used by the first identification means 702 by using a model different from the model learned by the learning means 701. The learning means 701 re-learns the model by using the training data including the label for the data determined based on the identification result derived by the second identification means 703 and the data.

Abstract: A flying body that can more reliably be made to hover at a desired position includes a determiner that determines whether to make the flying body hover, an image capturer that captures a periphery of the flying body, a recorder that records an image captured by the image capturer, and a stop controller that, if it is determined to make the flying body hover, stops the flying body in the air using the image recorded in the recorder and an image captured during flight.

Abstract: When an identification system serving as a model providing destination is determined, model selection means 603 selects models to be recommended to an operator as models to be integrated based on similarities between an attribute of data collection means included in the determined identification system and attributes of the data collection means included in the identification systems other than the determined identification system. Display control means 604 displays a screen for presenting the identification systems corresponding to the models selected by the model selection means 603 and the identification systems corresponding to the models which are not selected by the model selection means 603 to an operator. The operator can designate the identification systems from among the presented identification systems. Model integration means 602 generates a model by integrating the models corresponding to the identification systems designated by the operator on the screen.

Abstract: Concerning a partial area image that constitutes a wide area image, to control a flying body in accordance with a flight altitude at a past point of time of image capturing, an information processing apparatus includes a wide area image generator that extracts, from a flying body video obtained when a flying body captures a ground area spreading below while moving, a plurality of video frame images and combines the video frame images, thereby generating a captured image in a wide area, an image capturing altitude acquirer that acquires a flight altitude at a point of time of image capturing by the flying body for each of the plurality of video frame images, and an image capturing altitude output unit that outputs a difference of the flight altitude for each video frame image.

Abstract: Provided is an object detection device for efficiently and simply selecting an image for creating instructor data on the basis of the number of detected objects. The object detection device is provided with: a detection unit for detecting an object from each of a plurality of input images using a dictionary; an acceptance unit for displaying, on a display device, a graph indicating the relationship between the input images and the number of subregions in which the objects are detected, and displaying, on the display device, in order to create instructor data, one input image among the plurality of input images in accordance with a position on the graph accepted by operation of an input device; a generation unit for generating the instructor data from the input image; and a learning unit for learning a dictionary from the instructor data.

Abstract: Provided is a target member for accurately guiding a moving body to a target site. A target member 10 is used when performing control for guiding a moving body 20 to a target site, and the target member 10 is formed by using two or more different feature members 11, 12 that are set such that a shape of a target member image 44 that corresponds to the target member 10 captured by an image capturing unit 21 (an image capturing apparatus) that is mounted in the moving body 20 changes according to a measurement distance that indicates a distance between the target member 10 and the moving body 20.

Abstract: Provided are a moving body guidance apparatus, a moving body guidance method and a computer-readable recording medium that are for accurately guiding a moving body to a target site. A moving body guidance apparatus 1 has a detection unit 2 that detects a feature of a target member 30 from an image captured by an image capturing unit 23 mounted on a moving body 20, the feature changing according to a measurement distance indicating the distance between the moving body 20 and the target member 30, and a control unit 3 that performs control for guiding the moving body 20 to the target site 31 where the target member 30 is installed, based on the detected feature.

Abstract: Provided are a moving body guidance apparatus, a moving body guidance method and a computer-readable recording medium that are for accurately guiding a moving body to a target site. The moving body guidance apparatus has a detection unit 2 that detects, from an image 40 captured by an image capturing apparatus 23 mounted on a moving body 20, a target member image 42 captured of an entirety of a target member 30 or a feature member image 43 captured of an entirety or a portion of feature members 31 and 32 forming the target member 30, and a control unit 3 that performs guidance control for moving a set position 41 set with respect to the image 40 and indicating a position of the moving body 20 closer to the target member image 42 or closer to a designated region 44 set based on the feature member image 43.

Abstract: Concerning a partial area image that constitutes a wide area image, to measure an elapsed time from a past point of time of image capturing and control a flying body in accordance with the elapsed time, an information processing apparatus includes a wide area image generator that extracts, from a flying body video obtained when a flying body captures a ground area spreading below while moving, a plurality of video frame images and combines the video frame images, thereby generating a captured image in a wide area, an elapsed time measurer that measures an elapsed time from an image capturing time by the flying body for each of the plurality of video frame images, and an output unit that outputs the elapsed time for each video frame image together with position information of the flying body at the time of capturing of the video frame image.