عنوان

Stochastic Algorithms for Visual Tracking :

(Number (ISBN

1447106792

(Number (ISBN

1447111761

(Number (ISBN

9781447106791

(Number (ISBN

9781447111764

Number

b537019

Title Proper

Stochastic Algorithms for Visual Tracking :

General Material Designation

[Book]

Other Title Information

Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking

First Statement of Responsibility

by John MacCormick.

Place of Publication, Distribution, etc.

London

Name of Publisher, Distributor, etc.

Springer London

Date of Publication, Distribution, etc.

2002

Specific Material Designation and Extent of Item

.

Series Title

Distinguished Dissertations

Text of Note

1 Introduction and background.- 1.1 Overview.- 1.2 Active contours for visual tracking.- 1.2.1 Splines and shape space.- 1.2.2 Dynamical models using auto-regressive processes.- 1.2.3 Measurement methodology.- 2 The Condensation algorithm.- 2.1 The basic idea.- 2.2 Formal definitions.- 2.2.1 Technical detail: convergence of distribution-valued distributions.- 2.2.2 The crucial definition: how a particle set represents a distribution.- 2.3 Operations on particle sets.- 2.3.1 Multiplication by a function.- 2.3.2 Applying dynamics.- 2.3.3 Resampling.- 2.4 The Condensation theorem.- 2.5 The relation to factored sampling, or "where did the proof go?".- 2.6 "Good" particle sets and the effective sample size.- 2.6.1 The survival diagnostic.- 2.6.2 From effective sample size to survival diagnostic.- 2.6.3 Estimating the weight normalisation.- 2.6.4 Effective sample size of a resampled set.- 2.7 A brief history of Condensation.- 2.8 Some alternatives to Condensation.- 3 Contour likelihoods.- 3.1 A generative model for image features.- 3.1.1 The generic contour likelihood.- 3.1.2 The Poisson likelihood.- 3.1.3 The interior-exterior likelihood.- 3.1.4 The order statistic likelihood.- 3.1.5 The contour likelihood ratio.- 3.1.6 Results and examples.- 3.2 Background models and the selection of measurement lines.- 3.2.1 Discussion of the background model.- 3.2.2 Independence of measurement lines.- 3.2.3 Selection of measurement lines.- 3.3 A continuous analogue of the contour likelihood ratio.- 3.3.1 The continuous model.- 3.3.2 Likelihoods for HO and HB.- 3.3.3 Problems with the continuous ARP model.- 4 Object localisation and tracking with contour likelihoods.- 4.1 A brief survey of object localisation.- 4.2 Object localisation by factored sampling.- 4.2.1 Results.- 4.2.2 Interpretation of the gradient threshold.- 4.3 Estimating the number of targets.- 4.4 Learning the prior.- 4.5 Random sampling: some traps for the unwary.- 4.6 Tracker initialisation by factored sampling.- 4.6.1 Kalman filter tracker.- 4.6.2 The Condensation tracker.- 4.7 Tracking using Condensation and the contour likelihoods.- 4.7.1 The robustified colour contour likelihood.- 4.7.2 Implementation of a head tracker.- 5 Modelling occlusions using the Markov likelihood.- 5.1 Detecting occluded objects.- 5.2 The problem with the independence assumption.- 5.3 The Markov generative model.- 5.4 Prior for occlusions.- 5.5 Realistic assessment of multiple targets.- 5.5.1 Explanation of results.- 5.5.2 Experimental details.- 5.6 Improved discrimination with a single target.- 5.7 Faster convergence using importance sampling.- 5.8 Random samples using MelvIe.- 5.9 Calculating the partition functions.- 5.10 Further remarks.- 6 A probabilistic exclusion principle for multiple objects.- 6.1 Introduction.- 6.2 A generative model with an exclusion principle.- 6.2.1 Description of the generative model.- 6.2.2 Likelihoods derived from the generative model.- 6.2.3 Where does the "exclusion principle" come from?.- 6.2.4 The full likelihood.- 6.3 Tracking multiple wire-frame objects.- 6.4 Tracking multiple opaque objects.- 7 Partitioned sampling.- 7.1 The need for partitioned sampling.- 7.2 Weighted resampling.- 7.3 Basic partitioned sampling.- 7.4 Branched partitioned sampling.- 7.5 Performance of partitioned sampling.- 7.6 Partitioned sampling for articulated objects.- 7.6.1 Results: a vision-based drawing package.- 8 Conelusion?.- Appendix A.- A.1 Measures and Metrics on the configuration space.- A.2 Proof of the interior-exterior likelihood.- A.3 Del Moral's resampling lemma and its consequences.- Appendix B.- B.1 Summary Of Notation.

by John MacCormick.

John MacCormick

Electronic name

[Book]

Y