Regarding risks, at this point our most significant risks still lie with the technology. Although we’ve been able to find internet tutorials that seem fairly adaptable to our project, this doesn’t change the fact that none of us have worked with a Raspberry Pi before, and so we cannot be certain how that will go until we get our hands on it and start trying. Note: we’re planning to request the part(s) from inventory early this coming week. In terms of managing these risks, we’ve done all we can to find relevant supporting materials online. And, should things not go well, the contingency plan would be to reach out to course staff (TAs or faculty) sooner rather than later.
Regarding design changes, at our faculty meeting this week the possibility of using a NVIDIA Jetson was discussed. However, after some team discussion we are still planning to move forward with a Raspberry Pi, as the Jetson is considerably more expensive and definitely outside the consumer price point we’re seeking with this project. We expect that this may cause issues with latency – the ML and CV will definitely not be running with the best possible frame rate – but it is our belief that we can still get the project suitably functional on this hardware.
Regarding scheduling, at this point, no major schedule changes have occurred. This week has been about finishing up figuring out the details of how our implementation will work. Next week we plan to acquire the hardware and start working on more of the implementation itself.
Regarding principles of engineering, science, and math: One key consideration this week was price point. We want to engineer something that accomplishes our goal while remaining affordable. This is what informed our decision regarding our hardware, and pushed us towards using a Raspberry Pi over other more expensive alternatives. With regards to the ML classification, we have decided to implement a convolutional neural network. Neural networks are extremely common in image and speech recognition, and convolutional neural networks are particularly suited to image recognition due to their convolutional layer, a computation that reduces the dimensionality of image analysis without losing data. With the computer vision aspect, the aim is to reduce the ML workload by using simple pixel differences to identify when a moving component enters the frame, sending only that section to be identified only once, and then keeping track of it through the frame via OpenCV functionality after identification has been made. It is our hope that this will be more efficient than trying to run ML identification on every single frame. Information from the Raspberry Pi regarding movement summary and zone notifications will be sent to the user via a web application. With regards to web application development, we plan to use React on the frontend to display user options such as choosing forbidden zones for a pet, which will be implemented using a grid system overtop an image of the home, or request pet activity logs, which is done by organizing the data given by the Raspberry Pi into a heat map or time-sensitive graph, and use Django on the backend to store data related to these user options for each pet. One concern for the web application is privacy as live video feeds of homes can be displayed if requested by the user on the website, which we will address using the security protection features given by Django.