Team D2: xWalk

Progress Made

My sole focus area this week was the Look Light traffic light detection 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:

• Detecting circles in general
  • It took no time to detect a sole circle in a picture of a single circle. Detecting circles got harder with multiple circles in the picture. I was surprised to learn that the Hough Transform sometimes misses a circle within a group of concentric circles. I learned to optimize Hough transform radii parameters to detect all circles in images with multiple circles.
• Detecting circles in traffic light images
  • My learnings and parameterization of the Hough Transform for pictures of circles were transferred to use on images of traffic lights as well as traffic lights side-by-side (if you can imagine differently-facing traffic lights for perpendicular directions being hung right next to each other).
• Increasing accuracy in traffic light circle detection
  • I found out that the Hough Transform, in certain lighting conditions, sometimes “detects” curved edges as part of a circle and therefore “detects” circles that aren’t in the image. I am currently trying to determine, through empirical means, how to isolate just the physical light within the traffic light. I am leveraging the fact that the light of interest is illuminated, meaning that the respective pixels are of higher intensity (aka I can use some kind of filtering / thresholding). This allows me to run the Hough Transform on the isolated light portion of an image.

Progress

Reasonable progress made towards having a robust look light detection algorithm, so I believe I’m still on track. 🙂

Deliverables for Next Week

• Main priority: Keep optimizing the look light algorithm. Figure out how to best filter/threshold the traffic light image to isolate the light. Then, reassess and fine tune the Hough Transform parameters as needed.

Progress Made

My main focus this week was working with the traffic light detection algorithm’s validation set. A lot of the work was going in and hand tagging traffic lights – and with the suggestion of detecting more than just traffic lights (from our Design Review feedback) – crossing lights, cars, and other pedestrians. 

Additionally, I began working on the look direction traffic light detection algorithm, which takes in a cropped image of all the traffic lights in a scene and deduces which of the lights, if any, is the light the user is facing. My current implementation is by using the Hough circle transform (available in the opencv library) and looking for the traffic lights in which the more of the lights’ circular shapes are present given the angle at which the user is facing the light.

Progress

On a changed schedule – instead of myself and Yasaswini working on the traffic light detection algorithm first, I am working on the separate light state detection and look direction algorithms while Yasaswini, with Jeanette’s assistance, works on the light detection.

Deliverables for Next Week

• Zeroth priority: further refine (and FINALIZE) our requirements when meeting with Tom and Rashmi
• First priority: Continue working on and debugging the look direction algorithm

Progress Made

My main focus this week was with prepping and structuring the CV algorithm. I’ve been coding the training algorithm using the TensorFlow library and mainly debugging on smaller datasets (so I can run locally). 

I also helped the team finalize our parts order, which was sent out!

Progress

On Schedule! 🙂

Deliverables for Next Week

• Procure AWS credits and find out how to train model using AWS
• Train CV model on COCO dataset using TensorFlow
• Hopefully be able to do some validation with the photos I took too