Team D2: xWalk

PROGRESS MADE

My sole focus area on this week was mainly the Look Light traffic light detection cum Traffic Light Color algorithm. As mentioned last week, my most prominent objective was to increase the classification accuracy of the algorithm given the outputs from the CV algorithm.

To address these new problems and leverage the fact that the images reliably only contain (at least portions of) traffic lights, I explored how much more accuracy I could afford by scrapping circle detection altogether and primarily relying on color filtering and thresholding. In other words, this alternative algorithm would still filter on yellow, red, and green in series with thresholding but now use the argmax (argmax of {red, yellow, green}) of the sum of remaining pixels post-thresholding  as a means of determining the color.

Results

This approach did increase the classification accuracy. The previous iteration algorithm’s accuracy with the inputs passed in from the CV algorithm were unfortunately around 50% no our validation set, which is unacceptable. The new algorithm’s accuracy increased to nearly 90%, which was close to our overall goal metric from our Design. The full results  are reported below:

Overall:
Non-Look Light		14
Look Light		29
Total		43
Of Look:
	Actual R	Actual Y	Actual G
Output R	14	2	0
Output Y	0	2	0
Output G	1	0	10
Correct	0.896552
False Pos	0.034483
False Neg	0.068966

PROGRESS

The LookLight algorithm is now at a (more) acceptable state for the Final demo.  🙂

DELIVERABLES FOR NEXT WEEK

Fix any small problems when integrating.

Present / demo our findings! 🙂

PROGRESS MADE

My sole focus area this week was further Look Light traffic light detection cum Traffic Light Color algorithm. The one difference for this week versus previous weeks is that I’ve been using the snipped traffic light images from the CV algorithm output rather than the full scene.

My approach thus far was essentially applying my existing algorithm for each individual color filter (“red”, “green”, “yellow”), getting the number of circles identified and picking the color which resulted in the most circles.

This week, I tested the effectiveness of this algorithm against the outputs of the CV algorithm. Unfortunately, the accuracy was not consistent given new factors that have risen from the CV outputs. This wasn’t all bad news, however, because the detection (or lack thereof) of non-Look Lights was unchanged. The changes in accuracy just affected the classification of the color of the Look Lights.

The biggest issue I faced was with regards to the CV algorithm’s snipping itself. Sometimes the algorithm didn’t not capture all three lights in the traffic light (Problem 1). Sometimes the algorithm cut off a portion of the lights, so they were no longer entirely circular (Problem 2).

I was able to correct accuracy issues with regards to Problem 1, assuming that the illuminated light was preserved in its entirety. I simply modified my algorithm’s thresholding to ensure that just the illuminated light surpasses the threshold. Therefore, the output of this threshold, for all images with the entire illumated light, is basically a colored circle.

Problem 2 has been harder to solve, particularly because there are no longer complete circles. Rather the Hough Transform now looks for curved edges. Unfortunately, reflections of the colored illumination on the traffic light fixture also show up as colored, curved shapes. This is the largest current source of misclassification of color right now.

 

PROGRESS

Derailed a bit to adjust to new issues in snipped traffic light outputs. Hopeful it will be resolved in the coming days (see plan, below).

DELIVERABLES FOR NEXT WEEK

To address these new problems and leverage the fact that the images reliably only contain (at least portions of) traffic lights, I will see how much more accuracy I can afford by scrapping circle detection altogether and primarily relying on color filtering and thresholding. In other words, this alternative algorithm will still filter on yellow, red, and green in series with thresholding but now use the argmax (argmax of {red, yellow, green}) of the sum of remaining pixels post-thresholding  as a means of determining the color.