Experiment Design

Design Objectives

1. Determine the best threshold for temporal differencing to obtain minimal noise and maximum object motions
2. Determine the filter size to minimize noisy movements
3. Determine another filter size to eliminate background-drifted objects
4. Determine the best range parameter to obtain the best sets of regions before putting them into overlap-equivalence table
5. Determine the best template matching algorithm that will allow a high track rate of moving targets

Experiment 1: Determine the most optimal threshold and first filter size

Our first goal is to eliminate the false positives due to noisy movements in the motion image.
We accomplish this by:

1. Use size filter to eliminate the movements that are too small to be part of an object of interest.
2. Test it with synthetic images such as syn1car.vx and syn2car.vx and experiment with different size filter parameter to minimize the amount of false positives.
3. Next find a relationship between the size of the filter to the size of the image so that the program can determine what filter size to use automatically.

Experiment 2: Determine the most optimal second filter size and range parameter

Our second goal is to create the template.
We accomplish this by:

1. Use region growing to group moving pixels in motion image into motion regions and use size filter to eliminate regions that are too large and likely to belong to background.
2. Test with the synthetic images and experiment with different range parameter to obtain best sets of regions as well as different size filter as to minimize the amount of background drifting on to the object regions.
3. Group regions together that belong to same object by using equivalent table.
4. Test again. Repeat from step 2 until the right amount of objects are recognized, then create the template for each object.

Comments :
Since the synthetic images have no noise and can easily be segmented, they present no problem to our first and second goal hence we also test our algorithm with small real images such as truck2.vx and truck1.vx so to give a more accurate picture of the performance of our algorithm.

Experiment 3: Determine the most optimal template matching algorithm

Our third and final goal is to obtain the best template matching algorithm.
We accomplish this by:

1. Use minimum-absolute-difference matching to find the best matches between the templates and the grouped motion regions and use those best matches to update the templates themselves.
2. Test with synthetic images and small real images and experiment with different matching techniques.
3. We perform template matching on all the pixels in the image and compare the results with template matching only on those pixels that are moving. The results show the first method to be more time-consuming and the tracking results are almost identical for both methods.

Summary

In summary, we have four different parameters to experiment with:

• Threshold for temporal differencing to yield moving pixels of interest. We have determined this value to be about 10% of the brightest pixels.
• Lower size filter to eliminate the noisy movements. We have determined this to be approximately 0.14 % of the image size. This seems small but when object moves only those periphery pixels of the objects will be detected by temporal differencing hence small movement size.
• Upper size filter to eliminate background drifting on to the objects due to close intensity between the background and the object regions. We determine this to be approximately 3.4% of the image size.
• Range parameter for the region growing of the objects. We have determined this to be approximately 30 in brightness intensity which was found to work well across all images.