Abstract:
An information processing device according to the present invention includes a person extraction means that extracts a person in a captured image, an action extraction means that extracts an action of a person group including a plurality of persons other than a given person in the captured image, and an identification means that identifies a given person group based on a result of extraction of the action of the person group.
Abstract:
A passenger counting system that can count correctly the number of persons present in a vehicle, including persons sitting on the back seat, is provided. The passenger counting system includes an image capturing device 11 which is arranged outside the vehicle and which captures an image of the vehicle from the lateral direction of the vehicle, an image acquisition unit 31 that controls the image capturing device 11 and acquires an image in which the inside of the vehicle is captured as an input image, a profile detection unit 32 that detects a profile of a person from a partial image obtained from the input image based on an image feature amount calculated by using the partial image and outputs a detection result, and a passenger number determination unit 33 that determines the number of persons present in the vehicle based on the detection result.
Abstract:
An information processing apparatus is communicably connected to a link rule storage unit. The link rule storage unit stores a link rule. The link rule represents a criterion as to whether or not to link a plurality of pieces of trajectory data representing a trajectory of an object into one. Further, the link rule is a criterion for linking the plurality of pieces of trajectory data estimated to represent the trajectory of the same object. The information processing apparatus obtains a plurality of pieces of trajectory data, and classifies the obtained plurality of pieces of trajectory data into groups for each object. The information processing apparatus updates the link rule stored in the link rule storage unit by using the plurality of pieces of trajectory data classified into the groups.
Abstract:
An object detection system includes a reader waveguide (101) formed as an open transmission line, an RFID tag (102) placed above the reader waveguide (101), a signal strength acquisition unit (103) that acquires signal strength of a read signal read from the RFID tag (102) by electromagnetic coupling through the reader waveguide (101), and an object detection unit (104) that detects physical characteristics of an object (105) corresponding to the acquired signal strength based on a relationship between a plurality of (three or more values of) signal strength of the read signal and physical characteristics of the object to be detected.
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 machine learning apparatus 100 is an apparatus for constructing, by transfer learning, a second identification dictionary to be used in a second task from a first identification dictionary to be used in a first task. The machine learning apparatus 100 includes: a pseudo data generation unit 10 that generates pseudo data by processing one of real data in the first task and real data in the second task or both pieces of real data; and a learning unit 20 that constructs a third identification dictionary by performing, using the first identification dictionary, first transfer learning using the pseudo data as training data, and furthermore, constructs the second identification dictionary by performing, using the third identification dictionary, second transfer learning using the real data in the second task as training data.
Abstract:
The examination device 300 is equipped with a detection unit 310, a movement number calculation unit 320, and an estimation unit 330. The detection unit 310 detects identifier-carrying objects in each of first and second areas in which the identifier-carrying objects that are easy to identify are mixed with non-identifier-carrying objects that are hard to identify. The movement number calculation unit 320 calculates, on the basis of the detection results, the number of identifier-carrying objects that have moved from the first area into the second area. The estimation unit 330 estimates, on the basis of the calculated movement number of the identifier-carrying objects and the ratio between the identifier-carrying objects and the non-identifier-carrying objects in each area, the total number of the identifier-carrying objects and the non-identifier-carrying objects that have moved from the first area into the second area.
Abstract:
An RFID reading device according to the present invention includes: response requesting unit (20) configured to output a response request signal to a wireless tag; response reception unit (24) configured to extract an ID of the wireless tag and detecting a signal collision of a response from the wireless tag, based on a response signal output from the wireless tag in response to the response request signal; reading result acquisition unit (25) configured to acquire a read tag number indicating the number of wireless tags from which the corresponding ID has been successfully read; collision occurrence status acquisition unit (26) configured to count the number of time slots in which the signal collision has occurred and calculating the number of collisions; and reading result complementing unit (27) configured to estimate the number of readable wireless tags based on the read tag number and the number of collisions.
Abstract:
An object tracking system according to the present disclosure includes: object position detection means for detecting a position of an object by using a sensor; object tracking parameter storage means for storing a parameter related to an erroneous detection or a non-detection caused by a detection characteristic of the sensor; object tracking means for performing tracking based on the position obtained by the object position detection means and the parameter stored in the object tracking parameter storage means; object tracking result evaluation means for calculating an evaluation index based on a result obtained by the object tracking means; and object tracking parameter updating means for determining the parameter based on the evaluation index calculated by the object tracking result evaluation means and updating the parameter stored in the object tracking parameter storage means.
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).