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Shai Avidan's Support vector tracking and ensemble tracking

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CVPR 2001 and CVPR 2005 Shai Avidan's papers presented by Amir Rosenberger

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Tracking and Recognition Presented by: Amir Rosenberger Instructor : Dr. Lior Wolf Support Vector Tracking, Shai Avidan, CVPR 2001 Ensemble Tracking, Shai Avidan, CVPR 2005 Shai Avidan is credited for most of this presentation’s slides
What is Tracking? ,[object Object],[object Object],[object Object],[object Object]
Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Articles ,[object Object],[object Object],[object Object]
SVT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SVT Algorithm
SVT Algorithm Find a solution to
SVT Algorithm
Implementation ,[object Object],[object Object],[object Object],[object Object]
Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
[object Object]
[object Object],Initial: -8.3645 Final: 2.9272
Results Frame # SVM score
Results Frame # SVM score
Results Frame # SVM score
SVT ,[object Object]
Ensemble Tracking ,[object Object],[object Object],[object Object]
Ensemble Tracking ,[object Object],[object Object],[object Object],[object Object]
General Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object]
General Algorithm ,[object Object],[object Object],[object Object]
Train: Feature Space Time T
Test: Feature Space Time T+1
Track: Time T+1
Train Feature Space Time T+1
Train Feature Space Time T+1
Feature vector for every pixel ,[object Object],[object Object],[object Object],[object Object]
Object and Background ,[object Object],[object Object]
Confidence Measure ,[object Object],[object Object],[object Object],[object Object]
Mean Shift ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region This sequence was taken from a presentation of Yaron Ukrainitz & Bernard Sarel on mean shift
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Objective : Find the densest region
Integration over time 1 3 2 4 5 6
Integration over time 1 3 2 4 5 6
Learning by AdaBoost ,[object Object],[object Object]
Learning by AdaBoost ,[object Object],[object Object],[object Object]
Learning by AdaBoost ,[object Object],[object Object]
Learning by AdaBoost ,[object Object]
Outlier Rejection ,[object Object],[object Object]
Multi-resolution Tracking ,[object Object],[object Object],[object Object]
Tracking results
Tracking results
Changing weights Frame 10 Frame 40 Frame 70 Color Histogram Weak classifiers
Comparing fixed Vs. dynamic ensemble
Handle moving camera
even grayscale sequences
Handling Occlusions
Ensemble tracking ,[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object]
The End

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