Object detection method, object detection device, and program
AssigneeKonica Minolta, Inc.
InventorTaiki Sekii
Life of this patent
Estimating the term…
Abstract
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.
Claims15
An object detection method for detecting each object in an image containing one or more objects of one or more defined categories, comprising:
- a key point estimation step of estimating key point candidates for each object in the image;
- and
- a detection step of detecting key points for each object based on the estimated key point candidates, wherein
- 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, and
- two key points of the key points are defined for each of the coordinate axes, the two key points for each of the coordinate axes are a point on the boundary of the object having a maximum value and a point on the boundary of the object having a minimum value in a positive range of the each of the coordinate axes.
An object detection device for detecting each object in an image containing one or more objects of one or more defined categories, comprising:
- a machine-learning model trained to detect each object, which executes a key point estimation process of estimating key point candidates for each object in the image;
- and
- a detection unit that detects key points for each object based on the estimated key point candidates, wherein
- considering an object model that models shape of an object, the key points are points that satisfy a defined condition among points indicative of a boundary of the object model that are projected onto a defined coordinate axis, and
- 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, and
- two key points of the key points are defined for each of the coordinate axes, the two key points for each of the coordinate axes are a point on the boundary of the object having a maximum value and a point on the boundary of the object having a minimum value in a positive range of the each of the coordinate axes.
A non-transitory computer readable medium storing a program causing a computer to execute object detection processing for detecting each object in an image containing one or more objects of one or more defined categories, wherein
- the object detection processing comprises:
- a key point estimation step of estimating key point candidates for each object in the image;
- and
- a detection step of detecting key points for each object based on the estimated key point candidates, wherein
- considering an object model that models shape of an object, the key points are points that satisfy a defined condition among points indicative of a boundary of the object model that are projected onto a defined coordinate axis, and
- 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, and
- two key points of the key points are defined for each of the coordinate axes, the two key points for each of the coordinate axes are a point on the boundary of the object having a maximum value and a point on the boundary of the object having a minimum value in a positive range of the each of the coordinate axes.
Dependencies are inferred from the claim text.