Human pose estimation and tracking using label assignment
First Claim
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1. A method for estimating and tracking poses of a subject, the method comprising:
- generating data points of the subject from a first depth image including the subject by a depth camera, the first depth image representing distances from the depth camera to different parts of the subject;
grouping the data points into a plurality of segments of the data points based on, at least, depth profiles of the data points and spatial positions of the data points;
grouping the segments of the data points into labeled parts of the subject based on, at least, spatial relationships between the segments and constraint conditions by;
calculating a cost of assigning the segments to the labeled parts; and
assigning the segments to the labeled parts where the cost is minimized; and
fitting a model representing the subject to the data points using the groups to generate a pose vector representing the pose of the subject estimated from the data points.
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Abstract
A method and apparatus for estimating poses of a subject by grouping data points generated by a depth image into groups representing labeled parts of the subject, and then fitting a model representing the subject to the data points using the grouping of the data points. The grouping of the data points is performed by grouping the data points to segments based on proximity of the data points, and then using constraint conditions to assign the segments to the labeled parts. The model is fitted to the data points by using the grouping of the data points to the labeled parts.
62 Citations
18 Claims
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1. A method for estimating and tracking poses of a subject, the method comprising:
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generating data points of the subject from a first depth image including the subject by a depth camera, the first depth image representing distances from the depth camera to different parts of the subject; grouping the data points into a plurality of segments of the data points based on, at least, depth profiles of the data points and spatial positions of the data points; grouping the segments of the data points into labeled parts of the subject based on, at least, spatial relationships between the segments and constraint conditions by; calculating a cost of assigning the segments to the labeled parts; and assigning the segments to the labeled parts where the cost is minimized; and fitting a model representing the subject to the data points using the groups to generate a pose vector representing the pose of the subject estimated from the data points. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A non-transitory computer readable medium structured to store instructions executable by a processor, the instructions, when executed cause the processor to:
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generate data points of the subject from a first depth image including the subject by a depth camera, the first depth image representing distances from the depth camera to different parts of the subject; group the data points into a plurality of segments of the data points based on, at least, depth profiles of the data points and spatial positions of the data points; group the segments of the data points into labeled parts of the subject based on, at least, spatial relationships between the segments and constraint conditions by instructions to; calculate a cost of assigning the segments to the labeled parts; and assign the segments to the labeled parts where the cost is minimized; and fit a model representing the subject to the data points using the groups to generate a pose vector representing the pose of the subject estimated from the data points. - View Dependent Claims (12, 13, 14, 15)
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16. An apparatus for estimating and tracking poses of a subject, comprising:
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a depth camera configured generate a first depth image by capturing data points of the subject, the first depth image representing distances from the depth camera to different parts of the subject; a labeling module coupled to the depth camera, the labeling module configured to group the data points into a plurality of segments of the data points based on, at least, depth profiles of the data points and spatial positions of the data points, the labeling module further configured to group the segments of the data points into the labeled parts of the subject based on, at least, spatial relationships between the segments and constraint conditions, the labeling module comprising; a cost calculation module configured to calculate a cost of assigning the segments to the labeled parts; and a section adjustment module coupled to the cost assignment module and configured to assign the segments to the labeled parts where the cost is minimized; and a model fitting module coupled to the depth camera and the labeling module, the model fitting module configured to fit a model representing the subject to the data points. - View Dependent Claims (17, 18)
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Specification