Abstract: In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit.

Abstract: Based on operation trajectory data an operation analysis device identifies all open points indicating positions at which the crusher is opened during the operation period and all close points indicating positions at which a crusher is closed during an operation period, calculates, as a shortest distance, a distance between each open point of the all open points and a close point nearest to the each open point, and identifies, as a sorting destination open point, an open point at which the shortest distance exceeds a first threshold value, identifies data until the crusher grasping the dismantling part moves to the sorting destination and returns to the dismantling target again from among the operation trajectory data as movement data of the crusher having moved in the dismantling operation, and identifies data in which the movement data has been removed from the operation trajectory data as grasping operation data.

Abstract: An adhesive composition contains an acid anhydride group-containing oil or fat (A) and a curing agent (B) having a reactive group reactive with an acid anhydride group. A laminate is obtained by placing an adhesive layer between a first substrate and a second substrate, and the adhesive layer includes the aforementioned adhesive composition. A package is obtained by forming a bag from a laminate obtained by placing an adhesive layer between a first substrate and a second substrate, and the adhesive layer includes the aforementioned adhesive composition.

Abstract: The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: A sorting destination determining device extracts first records indicating a reversal to a slewing direction of a slewing body, extracts from records before the first records a second record indicating opening of an opening/closing state, extracts from the first records a third record immediately before which the second record is registered, and determines a sorting destination of a dismantled part on the basis of a slewing angle registered in the third record.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

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

Abstract: An information processing apparatus (10) includes an event detection unit (110), an input reception unit (120), and a processing execution unit (130). The event detection unit (110) detects a specific event from video data. The input reception unit (120) receives, from a user, input for specifying processing to be executed. The processing execution unit (103) executes first processing specified by input received by the input reception unit (120), and executes second processing of generating learning data used for machine learning and storing the generated learning data in a learning data storage unit (40).

Abstract: The information processing circuit 80 includes a first information processing circuit 81 that performs layer operations in deep learning, a second information processing circuit 82 that performs the layer operations in deep learning on input data by means of a programmable accelerator, and an integration circuit 83 integrates a calculation result of the first information processing circuit 81 with a calculation result of the second information processing circuit 82, and output an integration result, wherein the first information processing circuit 81 includes a parameter value output circuit 811 in which parameters of deep learning are circuited, and a sum-of-product circuit 812 that performs a sum-of-product operation using the input data and the parameters.

Abstract: The first parameter generation unit 811 generates a first parameter, which is a parameter of a first recognizer, using first learning data including a combination of data to be recognized, a correct label of the data, and domain information indicating a collection environment of the data. The second parameter generation unit 812 generates a second parameter, which is a parameter of a second recognizer, using second learning data including a combination of data to be recognized that is collected in a predetermined collection environment, a correct label of the data, and target domain information indicating the predetermined collection environment, based on the first parameter. The third parameter generation unit 813 integrates the first parameter and the second parameter to generate a third parameter to be used for pattern recognition of input data by learning using the first learning data.

Abstract: The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: The server device receives a model information from a plurality of terminal devices, and generates an integrated model by integrating the model information received from the plurality of terminal devices. The server device generates an updated model by learning a model defined by the model information received from the terminal device of update-target using the integrated model. Then, the server device transmits the model information of the updated model to the terminal device. Thereafter, the terminal device executes recognition processing using updated model.

Abstract: In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit.

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: An object detection apparatus 100 is provided with: a fish-eye image acquisition unit 10 configured to acquire a time series fish-eye image; a horizontal panorama image generation unit 20 configured to, for each frame, perform conversion to a horizontal panorama image in which a vertical direction in a real space is expressed in a perpendicular direction of the frame, and an azimuth is expressed equiangularly in a horizontal direction of the frame; an edge pair extraction unit 30 configured to extract a pair of edges in the perpendicular direction from the horizontal panorama image; a change rate extraction unit 40 configured to extract a change rate of an inter-edge distance between the pair of edges; a lower end region extraction unit 50 configured to extract a region of a lower end of an object providing the pair of edges; a distance change rate extraction unit 60 configured to calculate a distance from the object to the fish-eye camera based on the position of the region of the lower end of the object in

Abstract: An image acquisition unit 110 acquires a plurality of images. The plurality of images include an object to be inferred. An image cut-out unit 120 cuts out an object region including the object from each of the plurality of images acquired by the image acquisition unit 110. An importance generation unit 130 generates importance information by processing the object region cut out by the image cut-out unit 120. The importance information indicates the importance of the object region when an object inference model is generated, and is generated for each object region, that is, for each image acquired by the image acquisition unit 110. A learning data generation unit 140 stores a plurality of object regions cut out by the image cut-out unit 120 and a plurality of pieces of importance information generated by the importance generation unit 130 in a learning data storage unit 150 as at least a part of the learning data.