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EN0161 Image Understanding Course Project

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“Statistical Texture Model +

Line Search”,

A Fast Texture Boundary Detection Algorithm for Real-time Tracking

EN0161 Image Understanding Course Project

“Statistical Texture Model + Line Search”, A Fast Texture Boundary Detection Algorithm for Real-time Tracking

- What is the Problem?

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

- What is the Challenge?

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

EN0161 Image Understanding Course Project

“Statistical Texture Model + Line Search”, A Fast Texture Boundary Detection Algorithm for Real-time Tracking

Statistical Texture Model

+ Line Search

A texture is modeled as a statistical process which generates a sequence of pixels.

0th order model: the pixel intensities are independently drawn from a probability distribution;

1st order model: 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

“Statistical Texture Model + Line Search”, A Fast Texture Boundary Detection Algorithm for Real-time Tracking

P1

P2

Log(P1*P2)

Log(P1)

Log(P2)

The 0th Order model Result

The 0th model Result

The 0th Order model Result

The 0th Model Result

The 1th model for both target texture and background texture are known

The 1th model for both target texture and background texture are known

The 1th model for both target texture and background texture are known

The 1th model for both target texture and background texture are known

The texture is unknown

- 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.

The 3 Weeks Before Thanksgiving

The 3 Weeks

After

Thanksgiving

EN0161 Image Understanding Course Project

Object Tracking (Static Background)

Object Tracking (Static Background)

Object Tracking (Static Background)

Object Tracking (Non-Static Background)

Presentation_Video/DynamicBG.wmv

Supposedly, probability model based algorithms are able to work with Dynamic Background/Foreground!

Object Tracking (highly articulated structures)

A simple example of tracking moving object

Moving Texture Disk Tracking with 0th order Model

Presentation_Video/success.wmv

Moving Texture Disk Tracking with 0th order Model

(Texture is unknown. So the model is constructed online with the pixels from the search line. The result is not as good as known texture cases. )

Presentation_Video/HighlyTextured.wmv

Moving Texture Disk Tracking with 0th order Model

(Texture is unknown. So the model is constructed online with the pixels from the search line. It works in the dynamic texture model application!)

Presentation_Video/DynamicModel.wmv

Moving Texture Disk Tracking with 0th order Model

(The 0th order texture model will fail when the texture pixel intensity distribution is very close!)

Presentation_Video/fail_0th_model.wmv

Moving Texture Disk Tracking with 1st order Model

(The 1st order texture model takes the probability of adjacency of particular pixel intensities into consideration. So it’s result is much better than that of 0th order model)

Presentation_Video/checkborad.wmv

Moving Texture Disk Tracking with 1st order Model

(The 1st order texture model takes the probability of adjacency of particular pixel intensities into consideration. So it’s result is much better than that of 0th order model)

Presentation_Video/1vs0.wmv

How fast it is?

Optimization: Model Smoothing

EN0161 Image Understanding Course Project

Optimization: Model Smoothing

Assumption: Adjacent intensities have similar probabilities of appearing in a given texture. Therefore, we could approximate more accurate probability model of texture by smoothing the less accurate probability model from small learning area.

Optimization: Model Smoothing

So on and so forth, if we also apply the smoothing on 1st order model, supposedly the result will also be improved.

The Initialization Algorithm of Tracking.

2-D Texture Filter

OR

- How to find out the initial position of foreground?
- How to get the initial shape of foreground?

2 Dimensional Texture Filter:

Therefore, we find out the initial position of foreground and get the initial shape of foreground. And we can use the line search algorithm to start the object tracking.

Background/Foregroudn Matching with 0th and 1st order models.

Conclusion:

- The texture boundary detection algorithm mentioned in this paper works very well even in highly textured images. The 1st order Hidden Markov Model is more effective than intensity distribution model (0th order model).
- The algorithm could be applied in object tracking. When applied properly, it could run very fast;
- Because the algorithm is based on probability model, it could be used in the applications where the background or/both foreground is/are dynamic.