Predicting Joint Positions

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:

• 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.

A computer-implemented joint position prediction system comprising:

• 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.