Signal Processing - January 2017 - 37

of the racetrack. For example, detecting the obstacles on the
Curricula development based on the competition
racetrack, including hills and roadblocks, is generally easier
An important goal of the IMCC is to integrate the learning of
and more accurate by a camera than by other types of sensors.
multidisciplinary knowledge and the training of comprehensive
Despite the aforementioned advantages,
abilities important to engineering professionthere are challenges in performing image
als through the process of building and racing
Detection range is a
processing on a low-cost embedded platmodel cars. As mentioned earlier, the respecform. The main limitation comes from the
tive expertise involved include analog/digital
key factor affecting the
shortage of RAM memory capacity and the
electronics, embedded system, electronic
achievable speed of a
limited computing power of the MCU. To
design automation, control engineering, signal
self-navigating race car.
strike a good tradeoff between the processprocessing, pattern recognition, and more.
ing speed and accuracy, it is desirable for
Figure 16 illustrates the overall process for
the MCU to subsample the image into a lower resolution to
students to attend the competition, together with the respective
work with, and such low-resolution low-quality image poses a
disciplines supporting the different tasks in the competition.
challenge for student teams to reliably detect the racetrack and
other elements on the track.
Figure 14 shows an example scene of a model car on
the racetrack. Inset (a) is the binary image captured in the
MCU's RAM. Inset (b) shows the center line of the racetrack extracted from the image above by the image processing algorithm.
The image captured by the camera on board of a race car
also has a large distortion caused by the low viewing angle of
the camera. To obtain more accurate information of the road, a
perspective transformation is applied upon the image to correct
such distortion. Figure 15 shows the effect of perspective transformation. Figure 15(a) reveals the original grayscale image;
(b) shows the center line of the racetrack extracted from the
gray image; and (c) gives the correct center line after the perspective transformation.
In summary, signal processing techniques are applied
extensively on various aspects of the race cars. This compe(a)
(b)
tition allows students to practice their knowledge of signal
processing in task-oriented training scenarios, which comFigure 14. A model car with a camera mounted on the top of the pole in
plements the traditional classroom learning to better prepare
the middle of the car. The inset (a) shows the binary image captured in
students for future real-world R&D.
the RAM; (b) shows the center line of the racetrack.

2,500
250
2,000

200

1,500

150

1,000

100

500

50
0
(a)

100

200

300

(b)

-1,000 -500

0
(c)

500

1,000

Figure 15. (a) The origin image of the S-shape curved racetrack. (b) The center line of the racetrack. (c) The center line after the perspective transformation.
IEEE Signal Processing Magazine

January 2017

Table of Contents for the Digital Edition of Signal Processing - January 2017