Zebracorn Labs

Here at the Zebracorns, we believe in learning and pushing boundaries. In persuit of that, we have published papers, given talks, and have other tidbits of knowledge lying around. We hope you enjoy these as much as we do.

17 Dec 2016 - Adithya Balaji, Alon Greyber, Kevin Jaget

2016 FRC Workshops - Advanced Vision Presentation

How to set up and use a Jetson TX1 with advanced object recognition techniques. Covers hardware setup such as powering the board and connecting to the robot network. Also covers vision coding techniques used to find retroreflective tape targets commonly found in FRC games.

20 Sep 2016 - Marshall Massengill

The Rise of a new HERO

This project began as a thought exercise after one of our mentors had been playing with the new HERO board from Cross the Road Electronics (CTRE). The board was lightweight and versatile enough to be a very reliable robot control system.

23 Aug 2016 - Ryan Greenblatt

Positional Motion Profiling

This paper demonstrates team 900's efforts in using motion profiling on the Talon SRX. It contains a guide for motion profiling in LabVIEW with example code, but it is applicable to all languages. Questions about the paper can be posted here and someone who worked on this project will reply.

18 Aug 2016 - Alexander Allen

ZebraVision 4.0 - Neural Nets

Document describing the structure, implementation and utilization of neural networks for tracking game objects on the field in real time. We used the Caffe library from Berkeley Vision, the OpenCV library, and the DIGITS software from NVIDIA to create the neural network. This specific network was developed to track boulders for the FRC 2015-2016 season but can be applied to track virtually any object with proper data collection and training.

06 Jun 2016 - Ben Decker

ZebraVision 4.0 - Image Capturing

Zebravision 4.0 is Team 900s vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. This paper describes our methods of generating data for the neural networks. We took video of the ball using various lighting conditions and different, single-color backgrounds. We then identified the ball in these videos, took the clearest frames with the ball visible, and applied randomized shifts to the background in order to generate data.

23 May 2016 - Alon Greyber

ZebraVision 4.0 - Goal Detection

Zebravision 4.0 is Team 900's vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. One of the main features of Team 900's Zebravision code this year was goal detection. This paper gives an overview of the hardware and code used. The system used a Stereolabs ZED RGB-depth camera and green LED rings to highlight the retroreflective tape around the goal. The image was filtered to look for the reflected LED color and thresholded to turn it into binary green / not green image. The code then extracted contours from the image and applied a number of simple filters to rule out blobs which were obviously not goals. The remaining contours were scored in a number of criteria and the best scoring few objects were assumed to be goals. If more than one valid goal is found, several tiebreakers were used to pick one goal to shoot at. If a valid goal was found, the angle and distance to the target was reported; if none were found, a packet with -1.0 distance and angle was returned to the roboRIO.

19 May 2016 - Alon Greyber

ZebraVision 4.0 - Object Tracking

Zebravision 4.0 is Team 900s vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. This paper describes our tracking system, or how we get useful information that is persistent across frames from our detections.