Predicting Joint Positions
First Claim
1. A computer-implemented method of predicting joint positions comprising:
- receiving an input image of a scene comprising at least part of a human or animal body;
for each of a plurality of image elements of the input image, making a plurality of votes, each vote being for a position in the input image corresponding to a joint of the human or animal body;
the votes being made by comparing each image element with test image elements displaced therefrom by learnt spatial offsets; and
aggregating the votes to obtain at least one predicted joint position.
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Accused Products
Abstract
Predicting joint positions is described, for example, to find joint positions of humans or animals (or parts thereof) in an image to control a computer game or for other applications. In an embodiment image elements of a depth image make joint position votes so that for example, an image element depicting part of a torso may vote for a position of a neck joint, a left knee joint and a right knee joint. A random decision forest may be trained to enable image elements to vote for the positions of one or more joints and the training process may use training images of bodies with specified joint positions. In an example a joint position vote is expressed as a vector representing a distance and a direction of a joint position from an image element making the vote. The random decision forest may be trained using a mixture of objectives.
50 Citations
20 Claims
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1. A computer-implemented method of predicting joint positions comprising:
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receiving an input image of a scene comprising at least part of a human or animal body; for each of a plurality of image elements of the input image, making a plurality of votes, each vote being for a position in the input image corresponding to a joint of the human or animal body;
the votes being made by comparing each image element with test image elements displaced therefrom by learnt spatial offsets; andaggregating the votes to obtain at least one predicted joint position. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of training a random decision forest to produce votes for positions of joints of a human or animal body in an image comprising:
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receiving a plurality of training images having labeled joint positions; receiving at least one decision tree node splitting objective; selecting parameters for use at nodes of trees in the random decision forest by using the training images and the at least one objective; at each leaf node of each tree in the random decision forest obtaining a plurality of votes by applying the training images to the random decision forest with the selected parameters;
each vote being for a relative position in a training image predicted to correspond to a joint of the human or animal body;aggregating the votes at each leaf node by any of;
listing the votes, forming a histogram of votes, calculating a mean of the votes, and fitting a multi-modal distribution to the votes by any of expectation maximization, mean shift mode detection, k-means clustering and agglomerative clustering. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A computer-implemented joint position prediction system comprising:
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an input arranged to receive an input image of a scene comprising at least part of a human or animal body; a processor arranged, for each of a plurality of image elements of the input image, to make a plurality of votes, each vote being for a position in the input image corresponding to a joint of the human or animal body;
the processor being arranged to aggregate the votes to obtain at least one predicted joint position;
the processor being arranged to store each vote using a vector related to the direction and distance from an image element of the input image making the vote to a position in the input image where the joint is voted to be. - View Dependent Claims (18, 19, 20)
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Specification