A target detection device according to an embodiment includes an acquisition unit and a determination unit. The acquisition unit acquires positional data of a target in a plurality of sensors and sensor characteristic data that indicate a positional characteristic of detection accuracy in the plurality of sensors. The determination unit determines identity of a target object that is a detection object in each sensor based on positional data and sensor characteristic data that are acquired by the acquisition unit.

An information processing apparatus includes a depth image acquisition unit configured to acquire a depth image from a measurement apparatus that has measured a distance to an object, an image acquisition unit configured to acquire a captured image from an image capturing apparatus that has captured an image of the object, and an estimation unit configured to estimate a shape of the object based on the depth image and the captured image. The estimation unit acquires information about a contour of the object from the captured image, corrects the information about the contour based on the depth image, and estimates the shape of the object based on the corrected information about the contour.

An object detection method includes a key point estimation step of estimating key point candidates for each object in an image; and a detection step of detecting key points for each object based on the estimated key point candidates. Considering an object model that models shape of an object, the key points are points that satisfy a defined condition among points indicating a boundary of the object model that are projected onto defined coordinate axes. The defined coordinate axes have an origin at a geometric center of the object model and each forms a defined angle relative to a polar axis in a polar coordinate system set for the object model.

Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.

Virtual access control may include detecting entry of a person into a virtual controlled zone, and counting and/or identifying people including the person entering into the virtual controlled zone. Virtual access control may further include determining an authorization of the person to continue through the virtual controlled zone based on a facial identification of the person, and alerting the person to stop, exit from, or continue through the virtual controlled zone based on the determined authorization. An alarm may be generated if the person violates directions provided by the alert.

Many clothing listings, particularly in the secondhand market, lack comprehensive or standardized information about the product's attributes, making it difficult for consumers to find what they need. A method and server for classifying images depicting apparel items based on reference shapes is provided. Images depicting apparel are classified based on reference shapes and a geometrical model of the body. A method and system for querying images based on the classification is further provided.

A nail contour detecting device including a processor, wherein the processor obtains first feature point data of a first nail contour which is a nail contour detected from a first nail image obtained by imaging a nail of a finger or a toe, and second feature point data of a second nail contour which is a nail contour detected from a second nail image obtained by imaging a nail of the same finger or toe as the first nail image; and the processor obtains one nail contour based on the first feature point data and the second feature point data.

An information processing apparatus includes a depth image acquisition unit configured to acquire a depth image from a measurement apparatus that has measured a distance to an object, an image acquisition unit configured to acquire a captured image from an image capturing apparatus that has captured an image of the object, and an estimation unit configured to estimate a shape of the object based on the depth image and the captured image. The estimation unit acquires information about a contour of the object from the captured image, corrects the information about the contour based on the depth image, and estimates the shape of the object based on the corrected information about the contour.

A method for shape classification of an object is provided. Shape categories are provided which specify a plane and points therein relative to the object, and also specify at least one limit coordinate for each such point, the limit coordinate defining a boundary in a direction normal to the plane for the shape of the object considered in order for the object to be classified into a respective shape category. The shape categories can be provided by a user, making the method very flexible. The shape categories can in particular be derived from a set of samples of objects representing a shape category to be defined. For classification, the position of a surface of the object is measured at each of the points defined in the shape category, and the result is compared with the corresponding limit coordinate.

A method for acquiring traffic sign information includes acquiring an image of a scene comprising a traffic sign, the image being obtained by photographing the scene, using a photographing apparatus, acquiring first laser data of the scene, the first laser data being obtained by performing laser scanning on the scene, and the first laser data being of a plurality of first laser points, performing spatial clustering on the plurality of first laser points to obtain candidate point sets, acquiring a spatial distribution feature of respective laser points in each of the candidate point sets, determining at least one point set corresponding to the traffic sign in the candidate point sets, based on the spatial distribution feature, extracting image data of the traffic sign, from the image, using the at least one point set corresponding to the traffic sign, and extracting sign information of the traffic sign, from the image data.