초록 ▼

The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of movin

The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as `hypotheses`, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

대표청구항 ▼

[ What is claimed is:] [1.]1. A method of modeling an object using data collected as a sequence data frames, comprising:storing said data frames in a pixel by pixel data structure, said data structure written into a computer memory;selecting a set of starting points in a state space by a random sele

[ What is claimed is:] [1.]1. A method of modeling an object using data collected as a sequence data frames, comprising:storing said data frames in a pixel by pixel data structure, said data structure written into a computer memory;selecting a set of starting points in a state space by a random selection process, said state space used for computing a probability density function;computing a first probability density function in response to said starting points giving a probability that a model represents first data collected in a first frame stored as said data, said first probability density function plotted in state space, said state space having dimensions corresponding to parameters of said model;determining multiple peaks in said first probability density function, each said peak corresponding to a state space point, and each said state space point corresponding to a peak being referred to as a hypothesis point, and each peak corresponding to a maximum in said probability density function;computing, in response to a plurality of said hypothesis points, a second probability density function giving a probability that a model represents a second frame of data, said second frame of data stored in said data structure.