Author: lctummal

After items were picked up, I wanted to set up the camera and Jetson together to make sure they work, since initializing devices can often have compatibility problems. The first step was to flash the SD card with the image for the Linux operating system. Then, I inserted the microSD into the Jetson and connected the Jetson to a monitor. There were several issues with setup on the first attempt, related to running “sudo apt-get update.” This required me to flash the microSD, and to not update. I downloaded a demo program with “DepthAI” (which we likely will not use on the first attempt of writing code), and connected the camera. The result was a (somewhat) functional object detection. Currently, I am on track, since I anticipated setup to take more debugging efforts. Now, we can begin to run some smaller sample computer vision programs to determine the accuracy of card detection. I can also start profiling different methods of card detection, such as machine learning (which can usually guarantee a higher accuracy) and computer vision (which can be simpler but with no guarantee of accuracy).

As a team, we made final decisions for the camera and Jetson products, then placed orders for them. We are ready to start creating a program for card detection, as well as working on the math required for card counting. We are trying to find an easy way to develop without moving the hardware around too much. For example, it would be nice if we could develop significant parts of a computer vision program without needing to actually connect the camera. We’ll work on how to make this process easy. We do not have any significant schedule changes or block diagram changes. The most significant risks would be a hardware device breaking, which could delay progress as we may need to order a replacement. We still have a contingency plan to develop a CUDA kernel for additional performance if accuracy and performance suffer with computer vision or a python ML model.

 

Nicholas wrote part A, Lohith wrote part B, and Joe wrote part C.

(A) The product does not strictly meet a specified need to public health, safety, or welfare, but rather is designed to ensure these factors are not infringed upon. This product is meant to teach people how to count cards when playing Blackjack at a Casino, as well as providing a fun user experience with friends to all learn as a group. However, we could see that if a product like this gained wide spread adoption, without proper boundaries, arguably public welfare and health might decline. We note that gambling addictions are real problems that need to be properly addressed and mitigated when making a product such as our own, so we have designed it to have constant reminders of safety and proper risk management when playing Blackjack. Luckily, since we are teaching players how to optimally play Blackjack, we are giving them a lower chance of losing money and causing themselves financial harm, but we still need to be aware of the real risk and issues associated with a gambling addiction. Through occasional warnings during gameplay and a warning when starting the system, we hope to mitigate possible issues related to public health, safety and welfare.

(B) This product meets needs with consideration to social factors. Blackjack can be and is often a social activity. Many people going to the casino will go with friends or family, and playing blackjack is often a bonding experience, since a game involving both luck and skill will usually prompt discussion between members of the table, with feelings of jubilation (winning a hand), disappointment (a correct play leading to a loss), or surprise (a misplay or a lucky card). Our product, which aims to help a beginner player make the right decisions when playing, will also help the player take part in these discussions and therefore increase their social interaction with friends and family. Even a person who goes alone can start interacting with other players (including the dealer as is often the case), and maybe make some friends along the way. 

People also play blackjack with their friends at home, and it is possible that a beginner who isn’t confident enough in their skills to bet money will feel alienated. This product can help the beginner start making the right moves in blackjack and increase their confidence, enabling them to start betting and also having fun with their friends.

(C) This product does not really have that many economic considerations since it is an educational application that helps with making accurate probabilistic decisions. From an indirect perspective, the application does also help teach individuals how to manage their finances and make prudent financial decisions. However, from a strictly macro-economic perspective there are not that many relevant considerations for this application and thus the design of the product can more or less ignore this area.

Over the past week, I have been exploring options for camera models. The camera is an integral piece to our project, and since we have a goal of 90% accuracy for card identification, the choice of camera is very important. Out of the many options, one option I found was the Intel RealSense camera series. These cameras have sub-millimeter accuracy, which is sufficient for us, since this camera will be on a short tripod close to the hands of cards. Out of the many options, I settled upon the D435 model, a camera that records at 90 frames per second, with RGB capabilities, and a USB-C connector. Also, I have been researching videos on how to interface the Jetson Nano with the camera. This is a common practice that people do, so there is support for if we run into issues. I am currently on schedule. Early next week, we will place the order for the camera (unless the ECE department has one, in which case we will borrow one), and then we can start testing data transfer with the Jetson Nano with a small sample program. Then, we can start developing the ML model for identifying cards.