Image Processing Reference

In-Depth Information

Fig. 9.10
Tracking using SIFT. (
a
) Input. (
b
) Each
red cross
corresponds to the center of a small

rectangle containing pixels with high gradient information, i.e., a good region to track. 100 good

features are shown. (
c
)The
green lines
illustrate where the features have been refound in the new

image. Note that only ten features are shown to increase visibility

The following positions of an object have been detected:

i

e

1

2

3

4

5

6

7

8

9

10

11

12

13

X

1

2

4

5

4

6

8

9

9

7

3

2

2

Y

10

8

8

7

6

4

4

3

2

2

2

2

4

Exercise 2:
What is the velocity of the object at time

12?

Exercise 3:
What is the acceleration of the object at time

=

12?

Exercise 4:
Where will the object be predicted to be at time

=

=

12, if a first order

motion model is applied?

Exercise 5:
Where will the object be predicted to be at time

=

12, if a second order

motion model is applied?

Exercise 6:
It is desired to find the state of the object using information from both

the detection and the prediction. The uncertainty associated with the prediction

should be twice as high as the uncertainty associated with the detection. A first

order motion model is applied. What is the state at time

=

12?