Month: April 2021

Progress Made

My sole focus area this week was further Look Light traffic light detection cum Traffic Light Color algorithm. This image processing / pattern recognition based algorithm is based on the Hough transform, which returns the positions of circles of radii within a specified range as found within an input image. Because of the potential that the ML model would not be trained for the time being, I decided to work on using my existing algorithm to deduce the color of the light from the image of the whole scene as opposed to individual lights.

My approach 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.

Results:

• No significant change in accuracy *
• Increased latency (took 0.1-0.2 seconds to run for a full scene)

* On a side note, I was able to improve accuracy since the last status update by increasing the intensity of the median blur (larger kernel) to reduce the effect of noise that would be erroneously tagged as circular. (One example of such erroneous tagging, was the “detection” of circles in patches of leaves/shrubbery.)

With Yasaswini’s recent progress in her algorithm, my algorithm will no longer have to search for circles in the entire scene, as originally intended. I anticipate the accuracy to increase as I test my algorithm integrated with Yasaswini’s algorithm.

Progress

Progress made to actually output a color as opposed to a list of circles. Progress made to slightly improve accuracy. Seems like the right track. 🙂

Deliverables for Next Week

• Testing and further refinement with validation set traffic lights detected from Yasaswini’s algorithm, which she uploaded to the GitHub.

Progress Made

My sole focus area this week was further Look Light traffic light detection cum Traffic Light Color algorithm. This image processing / pattern recognition based algorithm is based on the Hough transform, which returns the positions of circles of radii within a specified range as found within an input image.

My work was broken up into the following areas:

• Light Exposure (concern for Look Light and State Detection)
  • A holdover problem from last week was with overexposed images in lower light settings (i.e. some of my validation photos taken at dusk). In these photos, the lights were white in color towards the center of the light, therefore not conserved when passed a red, yellow, and/or green filter. My first stab at fixing this was to regularize the maximum RGB intensity of  each photo through some simple pixelwise arithmetic with a reference intensity. This technique, after experimenting with various references intensities, yielded marginal improvement. Similarly, converting to HSV and doing something similar did not yield further significant improvements. Adding a white filter a la the red, yellow, and green filters did not yield any further improvement and actually worsened the accuracy for some photos.
    I have not yet tested using a threshold of RGB or HSV intensity in combination with any techniques above, so I will be testing this strategy next week.
• Look Angle (only problem for Look Light)
  • I experimented with various parameter ranges / thresholds in the Hough transforms to be more “lenient” in detecting curved edges that don’t strictly form perfect circles. In combination with red, yellow, green filters, there was some success, but the parameters need to be further fine tuned.
• Possible Amended Combined Approach
  • With regularization on the actual size of the traffic light (assuming that Yasaswini’s algorithm passes in the image of just the light in the scene to this algorithm), I am hypothesizing that simply applying each color filter and taking the sum of the resultant pixel values will yield the light color for the Look Light with the highest probability. I will test this out next week.

Progress

Not as much progress as desired. Improvements w.r.t. exposure and look angle were not as high as desired. Hopefully, the amended combined approach is a useful experiment. If accurate, it certainly will cut down on latency too, a metric I haven’t been paying too much attention to as of yet.

Deliverables for Next Week

• Experimentation with amended combined approach, above.
• Further improvements w.r.t exposure and look angle.

Progress Made

Continuing from last week, my sole focus area this week was the Look Light traffic light detection algorithm. Whereas, last week dealt more with me better understanding the Hough Transform itself, this past week dealt more with me fine tune the Transform parameterization and preprocessing image inputs to the Transform.

Problems going into this week (recap from my last report):

• Detecting multiple circles
• Detecting just circles instead of circles and various curved edges

After last week, I was able to partially resolve these issues, but circles were still being mislabeled in simpler images of traffic lights, in Fig 1 below. Notice how, for the yellow light, the transform detected the curved edge of the light fixture above the light (in which yellow light was reflected) rather than the light itself.

Preprocessing

• Image Size Regularization
  • The Hough Transform looks for circle with radii in a define range. Given that image sizes may vary, it is difficult to specify a range that will detect circles in the lights in every image of a traffic light. Through empirical means, I was able to resize incoming images to the same height dimensions (~400 px).
• Color Filtering
  • Because the Hough Transform is meant to detect the circular lights themselves, I can leverage the fact that said lights are colored red, yellow, or green. As such, I implemented these color filters, which dramatically increased accuracy in the simple image. The filters basically operate as masks that “filter” via a bitwise/pixelwise logical and. The thresholds from each masks are obtained by converting the image to HSV and looking for HSV values in pre-specified ranges of red, green, and yellow.

 

Testing on Less Perfect Images

• Presents new kinks to work out (various lighting environments seem to be posing the next biggest challenge) (see Deliverables section below)
• Success… but only using the red filter.
  • Problematic because each image needs to filter for all possible light colors. See current results for just red versus red, yellow, green filters applied below. (Notice how there’s a lot of green in the picture from the well exposed leaves.)

Progress

Look Detection algorithm progress may seem slow. However, because it incorporates color filtering, this progress may also help me with light color detection in the future. (It’s already distinguishing the colors of simple lights as show in Fig 2-4).

Deliverables for Next Week

• Main priority: Keep optimizing the look light algorithm to account for patches of high intensity / saturation.

Addendum