EN0161 Image Understanding Course Project

1
Statistical Texture Model Line Search, A
Fast Texture Boundary Detection Algorithm for
Real-time Tracking
EN0161 Image Understanding Course Project
2
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the problem?

Applications Real-time Tracking
Tracking the moving object
EN0161 Image Understanding Course Project
3
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the problem?

Applications Real-time Tracking
Tracking the non-moving object (camera is moving)
EN0161 Image Understanding Course Project
4
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the problem?

Applications Real-time Tracking
Offline Camera Registration from an Image
Sequence.
Automatic Camera Recovery for Closed or Open
Image Sequences
Obtain camera projection matrices and 3D
structure from long sequences of uncalibrated
images.
EN0161 Image Understanding Course Project
5
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the problem?

Applications Real-time Tracking
2D-3D model-based approaches
Robust real-time visual tracking using a 2D-3D
model-based approach
EN0161 Image Understanding Course Project
6
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the problem?

Applications Real-time Tracking
2D-3D model-based approaches
Stable Real-Time 3D Tracking using Online and
Offline Information
EN0161 Image Understanding Course Project
7
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is the Challenge?

• The target object and background are highly
  textured and contains too much clutter

• Most Object Tracking applications require
  real-time operation. So the computational
  efficiency of algorithm is very important.

EN0161 Image Understanding Course Project
8
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What are previous approaches?

Gabor Filters
EN0161 Image Understanding Course Project
9
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What are previous approaches?

Gabor Filters
An excellent descriptive tool for a wide range of
textures
Computationally exhaustive for real time
applications!
EN0161 Image Understanding Course Project
10
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What are previous approaches?

Multi-resolution algorithms
High speed without loss of accuracy.
Only effective for global segmentation of images
containing complex textures!
EN0161 Image Understanding Course Project
11
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• What is my approach?

Statistical Texture Model
Line Search
A texture is modeled as a statistical process
which generates a sequence of pixels. Model 1
the pixel intensities are independently drawn
from a probability distribution Model 2 1st
order Markov process (The probability of
selecting a given pixel intensity depends only on
the intensity of the preceding pixel)
EN0161 Image Understanding Course Project
12
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• Why this approach is better?

Speed Unlike other texture segmentation
techniques that require computing statistics over
image patches, this approach only require the
computing statistics over the searching line. So
it is very fast Actually, the implementation of
this approach results in a real-time 3D tracker
that uses only a fraction of the computational
resources of a modern PC, thus opening the
possibility to simultaneously track many objects
on ordinary hardware.
EN0161 Image Understanding Course Project
13
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• Why this approach is better?

Performance
EN0161 Image Understanding Course Project
14
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• Why this approach is better?

Performance
EN0161 Image Understanding Course Project
15
Statistical Texture Model Line Search, A Fast
Texture Boundary Detection Algorithm for
Real-time Tracking

• My plan

• Providing that the texture model for the target
  object and background are known, Implement the
  line search algorithm on the rendered texture
  boundaries to find the actual boundaries
• Providing that one of / both texture models are
  unknown, implement the model approximating
  algorithm
• Debug the algorithm in lightly textured
  environment first. Make sure that the algorithm
  is working in its proper way then test it in some
  highly textured environment
• Finish the texture segmentation process by
  adding the algorithm to find the fitting boundary
  line from the sequence of change points that we
  get from the previous steps.
• Evaluate the performance of texture segmentation
  in a single frame of image sequence.
• Evaluate the performance of object tracking in a
  continuous image sequences.

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EN0161 Image Understanding Course Project